Lead EXL's emerging technology and innovation agenda for AI and adjacent technologies. The role sets the direction for what EXL watches, evaluates, and builds, and owns the innovation pipeline from first scan to a demonstrated, recommended capability. The incumbent demonstrates validated innovations and recommends which technologies should advance, pause, or stop. This is a hands-on technical leadership role. The incumbent reviews evaluations and prototypes, challenges assumptions with evidence, and personally builds reference demonstrations for senior audiences.
A well-governed innovation portfolio in which every emerging technology carries a clear status (adopt, pilot, monitor, or drop), a rationale, and an owner, with each status recommendation backed by evidence.
A steady flow of validated innovations, each with a benchmarked case, reusable assets, and a recommended handover path to the AI Platforms team.
Faster and better-informed technology decisions for the VP of Enterprise AI Engineering and the Architecture and Engineering group, supported by consistent evaluation standards.
Early-mover advantage in client workshops, proposals, and demonstrations, grounded in technologies EXL has tested rather than read about.
Credible external presence through publications, conference talks, IP disclosures, and partner technology engagement.
1. Innovation Strategy and Radar Governance
Define the emerging technology agenda aligned with EXL's industry focus (Healthcare, Insurance, Banking, and Financial Services) and the Global Technology strategy.
Maintain the technology radar and propose stage classifications and the criteria for moving a technology between stages, for approval by the VP of Enterprise AI Engineering.
Recommend which emerging technologies deserve effort and which should be retired, with the reasoning recorded.
2. Evaluation Standards and Recommendations
Set the evaluation framework covering functional fit, scalability, security and compliance, licensing, total cost, vendor viability, and community health.
Review evaluation briefs, prototypes, and go/no-go recommendations before they are presented to the VP of Enterprise AI Engineering.
Recommend build, buy, partner, or adopt options for new tools, frameworks, and platforms, with clear trade-off documentation for the decision.
3. Prototyping Portfolio and Proof of Value
Propose proof-of-value criteria, baselines, and test datasets for each prototype, and agree them with the VP of Enterprise AI Engineering before prototyping begins.
Review benchmark results and challenge conclusions that rest on weak evidence.
Ensure prototypes meet quality, safety, and Responsible AI checks before they are demonstrated to clients or put forward for production.
4. Technology Transfer and Platform Alignment
Prepare handover packages for validated innovations to the AI Platforms Lead (D2), with production-readiness criteria and integration guidance.
Coordinate priorities with Product, Engineering, Data, Cloud, and Security teams so innovation work is not duplicated or stranded.
Track adoption of innovations by platform and solution teams and raise barriers with the AI Platforms Lead (D2), with recommended actions.
5. Client, Pursuit, and Executive Engagement
Lead technical discovery and innovation showcases for strategic opportunities.
Contribute the innovation narrative and technical differentiation to RFP and RFI responses.
Demonstrate the emerging technology landscape and its implications for EXL's business to senior technology leaders.
6. Team Leadership and Development
Lead, mentor, and develop the Senior R&D Engineers (D1) in the unit, setting expectations for rigor, documentation, and speed.
Run portfolio reviews, technology reviews, and innovation forums across the Architecture and Engineering group.
Propose budget, compute, tool licensing, and trial access needs for the R&D unit, for approval through the VP of Enterprise AI Engineering.
7. External Visibility and IP
Drive patent disclosures, white papers, conference submissions, and analyst or partner engagement on AI innovation.
Represent EXL in technical discussions with academic institutions, technology vendors, and ecosystem partners.
Emerging AI Technologies: Deep, current knowledge of generative AI, agentic AI, retrieval, context and memory systems, and developer tooling, with the ability to assess new technologies quickly and critically.
Architecture and Evaluation: Strong architectural judgment for assessing technologies against enterprise requirements, including scalability, security, cost, and operability.
Hands-on Technical Depth: Working proficiency in Python and LLM application development, sufficient to review prototypes and evaluation code with credibility.
Experimental Methods: Grounding in benchmarking, experimental design, and comparison studies for AI systems.
Cloud and Platform Awareness: Working knowledge of AI and cloud-native services on Azure, AWS, or GCP, and of containerized deployment.
Responsible AI and Security: Understanding of guardrails, safety evaluation, privacy, and regulatory considerations for new technologies.
Research Communication: Ability to translate technical findings into clear demonstrations, recommendations, and narratives for engineers, executives, and clients.
15-18 years of experience across AI engineering, applied research, software architecture, or technology innovation, with at least 5 years in a technology evaluation, innovation, or research leadership capacity.
A demonstrated record of identifying, evaluating, and bringing new technologies into production or pilot use.
Publications, patents, open-source contributions, or conference presentations in AI or related fields are strongly preferred.
Experience in Healthcare, Insurance, Banking, or Financial Services is preferred.
Master's or PhD in Computer Science, AI, Data Science, or a related discipline preferred; a Bachelor's degree with equivalent experience will be considered.
Number of emerging technologies evaluated, with clear and defensible adopt, pilot, or drop recommendations accepted by the VP of Enterprise AI Engineering.
Share of demonstrated innovations adopted by the AI Platforms team or used in client solutions.
Quality and reuse of validated assets, benchmarks, and evaluation standards across the group.
Patents filed, publications, and conference presentations.
Measurable contribution of innovation to proposals, client outcomes, and platform capability.
Growth of direct reports, including mentoring outcomes and internal knowledge sharing.
Lead EXL's emerging technology and innovation agenda for AI and adjacent technologies. The role sets the direction for what EXL watches, evaluates, and builds, and owns the innovation pipeline from first scan to a demonstrated, recommended capability. The incumbent demonstrates validated innovations and recommends which technologies should advance, pause, or stop. This is a hands-on technical leadership role. The incumbent reviews evaluations and prototypes, challenges assumptions with evidence, and personally builds reference demonstrations for senior audiences.
A well-governed innovation portfolio in which every emerging technology carries a clear status (adopt, pilot, monitor, or drop), a rationale, and an owner, with each status recommendation backed by evidence.
A steady flow of validated innovations, each with a benchmarked case, reusable assets, and a recommended handover path to the AI Platforms team.
Faster and better-informed technology decisions for the VP of Enterprise AI Engineering and the Architecture and Engineering group, supported by consistent evaluation standards.
Early-mover advantage in client workshops, proposals, and demonstrations, grounded in technologies EXL has tested rather than read about.
Credible external presence through publications, conference talks, IP disclosures, and partner technology engagement.
1. Innovation Strategy and Radar Governance
Define the emerging technology agenda aligned with EXL's industry focus (Healthcare, Insurance, Banking, and Financial Services) and the Global Technology strategy.
Maintain the technology radar and propose stage classifications and the criteria for moving a technology between stages, for approval by the VP of Enterprise AI Engineering.
Recommend which emerging technologies deserve effort and which should be retired, with the reasoning recorded.
2. Evaluation Standards and Recommendations
Set the evaluation framework covering functional fit, scalability, security and compliance, licensing, total cost, vendor viability, and community health.
Review evaluation briefs, prototypes, and go/no-go recommendations before they are presented to the VP of Enterprise AI Engineering.
Recommend build, buy, partner, or adopt options for new tools, frameworks, and platforms, with clear trade-off documentation for the decision.
3. Prototyping Portfolio and Proof of Value
Propose proof-of-value criteria, baselines, and test datasets for each prototype, and agree them with the VP of Enterprise AI Engineering before prototyping begins.
Review benchmark results and challenge conclusions that rest on weak evidence.
Ensure prototypes meet quality, safety, and Responsible AI checks before they are demonstrated to clients or put forward for production.
4. Technology Transfer and Platform Alignment
Prepare handover packages for validated innovations to the AI Platforms Lead (D2), with production-readiness criteria and integration guidance.
Coordinate priorities with Product, Engineering, Data, Cloud, and Security teams so innovation work is not duplicated or stranded.
Track adoption of innovations by platform and solution teams and raise barriers with the AI Platforms Lead (D2), with recommended actions.
5. Client, Pursuit, and Executive Engagement
Lead technical discovery and innovation showcases for strategic opportunities.
Contribute the innovation narrative and technical differentiation to RFP and RFI responses.
Demonstrate the emerging technology landscape and its implications for EXL's business to senior technology leaders.
6. Team Leadership and Development
Lead, mentor, and develop the Senior R&D Engineers (D1) in the unit, setting expectations for rigor, documentation, and speed.
Run portfolio reviews, technology reviews, and innovation forums across the Architecture and Engineering group.
Propose budget, compute, tool licensing, and trial access needs for the R&D unit, for approval through the VP of Enterprise AI Engineering.
7. External Visibility and IP
Drive patent disclosures, white papers, conference submissions, and analyst or partner engagement on AI innovation.
Represent EXL in technical discussions with academic institutions, technology vendors, and ecosystem partners.
Emerging AI Technologies: Deep, current knowledge of generative AI, agentic AI, retrieval, context and memory systems, and developer tooling, with the ability to assess new technologies quickly and critically.
Architecture and Evaluation: Strong architectural judgment for assessing technologies against enterprise requirements, including scalability, security, cost, and operability.
Hands-on Technical Depth: Working proficiency in Python and LLM application development, sufficient to review prototypes and evaluation code with credibility.
Experimental Methods: Grounding in benchmarking, experimental design, and comparison studies for AI systems.
Cloud and Platform Awareness: Working knowledge of AI and cloud-native services on Azure, AWS, or GCP, and of containerized deployment.
Responsible AI and Security: Understanding of guardrails, safety evaluation, privacy, and regulatory considerations for new technologies.
Research Communication: Ability to translate technical findings into clear demonstrations, recommendations, and narratives for engineers, executives, and clients.
15-18 years of experience across AI engineering, applied research, software architecture, or technology innovation, with at least 5 years in a technology evaluation, innovation, or research leadership capacity.
A demonstrated record of identifying, evaluating, and bringing new technologies into production or pilot use.
Publications, patents, open-source contributions, or conference presentations in AI or related fields are strongly preferred.
Experience in Healthcare, Insurance, Banking, or Financial Services is preferred.
Master's or PhD in Computer Science, AI, Data Science, or a related discipline preferred; a Bachelor's degree with equivalent experience will be considered.
Number of emerging technologies evaluated, with clear and defensible adopt, pilot, or drop recommendations accepted by the VP of Enterprise AI Engineering.
Share of demonstrated innovations adopted by the AI Platforms team or used in client solutions.
Quality and reuse of validated assets, benchmarks, and evaluation standards across the group.
Patents filed, publications, and conference presentations.
Measurable contribution of innovation to proposals, client outcomes, and platform capability.
Growth of direct reports, including mentoring outcomes and internal knowledge sharing.
Lead EXL's emerging technology and innovation agenda for AI and adjacent technologies. The role sets the direction for what EXL watches, evaluates, and builds, and owns the innovation pipeline from first scan to a demonstrated, recommended capability. The incumbent demonstrates validated innovations and recommends which technologies should advance, pause, or stop. This is a hands-on technical leadership role. The incumbent reviews evaluations and prototypes, challenges assumptions with evidence, and personally builds reference demonstrations for senior audiences.
A well-governed innovation portfolio in which every emerging technology carries a clear status (adopt, pilot, monitor, or drop), a rationale, and an owner, with each status recommendation backed by evidence.
A steady flow of validated innovations, each with a benchmarked case, reusable assets, and a recommended handover path to the AI Platforms team.
Faster and better-informed technology decisions for the VP of Enterprise AI Engineering and the Architecture and Engineering group, supported by consistent evaluation standards.
Early-mover advantage in client workshops, proposals, and demonstrations, grounded in technologies EXL has tested rather than read about.
Credible external presence through publications, conference talks, IP disclosures, and partner technology engagement.
1. Innovation Strategy and Radar Governance
Define the emerging technology agenda aligned with EXL's industry focus (Healthcare, Insurance, Banking, and Financial Services) and the Global Technology strategy.
Maintain the technology radar and propose stage classifications and the criteria for moving a technology between stages, for approval by the VP of Enterprise AI Engineering.
Recommend which emerging technologies deserve effort and which should be retired, with the reasoning recorded.
2. Evaluation Standards and Recommendations
Set the evaluation framework covering functional fit, scalability, security and compliance, licensing, total cost, vendor viability, and community health.
Review evaluation briefs, prototypes, and go/no-go recommendations before they are presented to the VP of Enterprise AI Engineering.
Recommend build, buy, partner, or adopt options for new tools, frameworks, and platforms, with clear trade-off documentation for the decision.
3. Prototyping Portfolio and Proof of Value
Propose proof-of-value criteria, baselines, and test datasets for each prototype, and agree them with the VP of Enterprise AI Engineering before prototyping begins.
Review benchmark results and challenge conclusions that rest on weak evidence.
Ensure prototypes meet quality, safety, and Responsible AI checks before they are demonstrated to clients or put forward for production.
4. Technology Transfer and Platform Alignment
Prepare handover packages for validated innovations to the AI Platforms Lead (D2), with production-readiness criteria and integration guidance.
Coordinate priorities with Product, Engineering, Data, Cloud, and Security teams so innovation work is not duplicated or stranded.
Track adoption of innovations by platform and solution teams and raise barriers with the AI Platforms Lead (D2), with recommended actions.
5. Client, Pursuit, and Executive Engagement
Lead technical discovery and innovation showcases for strategic opportunities.
Contribute the innovation narrative and technical differentiation to RFP and RFI responses.
Demonstrate the emerging technology landscape and its implications for EXL's business to senior technology leaders.
6. Team Leadership and Development
Lead, mentor, and develop the Senior R&D Engineers (D1) in the unit, setting expectations for rigor, documentation, and speed.
Run portfolio reviews, technology reviews, and innovation forums across the Architecture and Engineering group.
Propose budget, compute, tool licensing, and trial access needs for the R&D unit, for approval through the VP of Enterprise AI Engineering.
7. External Visibility and IP
Drive patent disclosures, white papers, conference submissions, and analyst or partner engagement on AI innovation.
Represent EXL in technical discussions with academic institutions, technology vendors, and ecosystem partners.
Emerging AI Technologies: Deep, current knowledge of generative AI, agentic AI, retrieval, context and memory systems, and developer tooling, with the ability to assess new technologies quickly and critically.
Architecture and Evaluation: Strong architectural judgment for assessing technologies against enterprise requirements, including scalability, security, cost, and operability.
Hands-on Technical Depth: Working proficiency in Python and LLM application development, sufficient to review prototypes and evaluation code with credibility.
Experimental Methods: Grounding in benchmarking, experimental design, and comparison studies for AI systems.
Cloud and Platform Awareness: Working knowledge of AI and cloud-native services on Azure, AWS, or GCP, and of containerized deployment.
Responsible AI and Security: Understanding of guardrails, safety evaluation, privacy, and regulatory considerations for new technologies.
Research Communication: Ability to translate technical findings into clear demonstrations, recommendations, and narratives for engineers, executives, and clients.
15-18 years of experience across AI engineering, applied research, software architecture, or technology innovation, with at least 5 years in a technology evaluation, innovation, or research leadership capacity.
A demonstrated record of identifying, evaluating, and bringing new technologies into production or pilot use.
Publications, patents, open-source contributions, or conference presentations in AI or related fields are strongly preferred.
Experience in Healthcare, Insurance, Banking, or Financial Services is preferred.
Master's or PhD in Computer Science, AI, Data Science, or a related discipline preferred; a Bachelor's degree with equivalent experience will be considered.
Number of emerging technologies evaluated, with clear and defensible adopt, pilot, or drop recommendations accepted by the VP of Enterprise AI Engineering.
Share of demonstrated innovations adopted by the AI Platforms team or used in client solutions.
Quality and reuse of validated assets, benchmarks, and evaluation standards across the group.
Patents filed, publications, and conference presentations.
Measurable contribution of innovation to proposals, client outcomes, and platform capability.
Growth of direct reports, including mentoring outcomes and internal knowledge sharing.
