AI/ML Engineer

Role Overview

• Work across diverse GenAI platforms like AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms and libraries.
• Automate workflows involving extraction of complex, multimodal unstructured content from variety of sources in to highly accurate and reliable structured content using platforms like AWS Textract and Bedrock
• Design and build MCP hosts, clients and servers
• Establish and use frameworks for automated LLM testing
• Create regression test suites to detect drift or prompt breakage
• Integrate with internal and external web services using secure authentication and authorization mechanisms
• Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines
• Design, develop, and deploy production-grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases.
• Design, maintain, and optimize end-to-end AI/ML pipelines including data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure (e.g., AWS or equivalent)
• Ensure AI/ML solutions are scalable, reliable, secure, and cost-effective within cloud environments
• Create reusable components, frameworks, and best practices to accelerate AI development
• Partner with data scientists, architects, product managers, business stakeholders and technical teams across organization to align AI solutions with organizational goals.
• Provide hands-on technical support and mentorship to technical teams across the enterprise.

Requirements

• Bachelor’s degree in computer science, Engineering, or related field.
• 3+ years of experience designing and deploying ML/AI solutions in real-world environments
• Very strong Python skills with strong hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic, etc.) using Python and Python based frameworks
• Strong hands-on experience in prompt engineering, context construction, grounding strategies
• Strong hands-on experience with Retrieval Augmented Generation (RAG) extracting, chunking and create embeddings from unstructured documents from diverse sources including O365(email, word, excel), PDFs, and webpages.
• Comfortable building Model Context Protocol (MCP) clients, servers and hosts.
• Strong Expertise in building REST APIs and integrating with internal/external APIs
• Hands-on experience with Intelligent Document Processing and/or OCR technologies on complex documents
• Knowledge of Google A2A
• Deep experience in AWS (Lambda, Bedrock, Step Functions, API Gateway, IAM)
• Strong experience with Observability tools like Dynatrace, or other similar GenAI observability tools
• Excellent GenAI foundations and concepts
• Clear understanding of enterprise data privacy, AI governance, and observability
• Proficiency in Python and common ML/AI libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong understanding of data engineering, SQL, and feature engineering.
• Hands-on experience with cloud services such as AWS Sagemaker, Lambda, ECS, S3, and IAM.
• Familiarity with containerization (Docker) and orchestration (e.g., Airflow, Kubeflow).
• Working with version control and collaboration tools (Git, Jira, Confluence etc).

Tech Stack

• Airflow
• AWS
• Azure
• Cloud
• Docker
• Oracle
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Health insurance
• Retirement plans
• Paid time off
• Flexible work arrangements
• Professional development