Forward Deployed Engineer

Company Description

SIA is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today, our strategy and management capabilities are augmented by data science, enhanced by creativity, and driven by responsibility. We are optimists for change, and we help clients initiate, navigate, and benefit from transformation.

With expertise across a broad range of sectors and services, our consultants serve clients worldwide from offices in many countries. Our AI and Data teams combine data science, generative AI, and engineering to take solutions from proof of concept to large-scale deployment.

Our Mumbai office launched in 2024. We are building the team with people who want to shape something from the ground up, combining the agility of a start-up with the backing and reach of a global brand. The AI Factory in Mumbai builds and delivers production-grade Generative AI and Agentic AI solutions for enterprise clients across North America, Europe and the Middle East.

Job Description

This role combines hands-on software engineering with client-facing delivery. The Forward Deployed Engineer turns business requirements into production-ready AI solutions, working within the client's engineering context and standards, and owns agreed deliverables from technical discovery through deployment and handover.
You will close the gap between a Generative AI prototype and a solution that runs reliably in a real enterprise environment. You will be trusted to represent Sia in technical conversations with client architects, security teams, and product owners.

Key Responsibilities:

• Work embedded with client and Sia teams, taking part in planning, stand-ups, and delivery activities.
• Run technical discovery with client stakeholders and turn business needs into scoped, buildable deliverables with clear acceptance criteria.
• Build production-grade Python services and APIs (FastAPI and REST) that power Generative AI applications, including RAG pipelines over enterprise knowledge sources.
• Integrate LLM APIs and SDKs into client environments, handling authentication, rate limits, fallbacks, latency, and cost.
• Connect solutions to enterprise systems such as databases, internal APIs, document stores, and business applications.
• Deploy and release services on AWS, Azure, or GCP using Docker and CI/CD, working within the client's codebase and engineering standards.
• Apply testing, observability and monitoring; lead production troubleshooting and root-cause analysis.
• Apply security, reliability and performance practices from the start, including secrets handling, access control and data protection.
• Estimate effort, flag risks and dependencies early, and communicate trade-offs and progress openly to technical and non-technical stakeholders.
• Document decisions, hand over knowledge, and train client teams so the solution can be operated and adopted.
• Capture reusable components and lessons from each engagement and contribute them back to the AI Factory.

Qualifications

Education and experience:

• Bachelor's or Master's degree in Computer Science, Engineering or a related field, or equivalent practical experience.
• 3 to 6 years of professional software engineering experience, with a focus on Python backend development.
• At least one Generative AI or LLM-based solution delivered to production, with clear personal ownership.
• Experience in governed enterprise environments, including security reviews and change processes.
• Fluent professional English, written and spoken.

Technical skills (required):

• Strong Python with production experience, including FastAPI or an equivalent web framework and REST API design.
• Backend and microservices design: service boundaries, asynchronous processing, error handling, and automated testing.
• Ability to work productively in unfamiliar codebases and follow existing engineering standards.
• SQL and relational data modeling, and working experience with at least one vector database or vector search capability.
• Practical experience with LLM applications: provider APIs and SDKs, RAG, tool calling, and prompt design.
• Deployment and operation on at least one major cloud (AWS, Azure or GCP), with Docker, Git and CI/CD.
• Observability and production support: structured logging, metrics, tracing, and root-cause analysis.
• Working knowledge of application security and reliability, including authentication, secrets management, and performance tuning.

Consulting and client engagement:

• Ability to understand business challenges and translate them into technical requirements.
• Clear communication with technical and non-technical stakeholders, including in workshops, technical discussions, and demonstrations.
• Comfort working embedded in a client team and building trust through accountability and delivery quality.

Good to have

• Agent orchestration and tool-calling frameworks (LangGraph, LangChain, OpenAI or Claude SDKs) and the Model Context Protocol (MCP).
• Experience with enterprise GenAI platforms and managed AI services on AWS, Azure or GCP.
• Kubernetes, infrastructure-as-code, and NoSQL databases.
• Spark or Databricks for data-intensive solutions.
• Consulting or client-facing experience, or experience in regulated sectors such as financial services, insurance, or life sciences.

Additional Information

What We Offer 

• The opportunity to work on production AI for global enterprise clients, as part of a growing AI engineering capability.
• The chance to lead AI delivery in a global consulting setting and build breadth across the full lifecycle, from discovery to production.
• Access to Sia's leadership and training programs as you grow.
• A collaborative, multidisciplinary team working across varied projects and industries.

Position based in Mumbai (onsite)

Sia is an equal opportunity employer. Decisions on hiring, promotion, remuneration, and discipline are made solely on performance, competence, conduct, and business needs. 

Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.