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EXL
AI Engineer
Data Scientist
On-siteSeniorAI Engineerposted 1w ago
Role summaryAI-generated
This role leads the design and deployment of multi-agent generative AI solutions, building high-performance APIs with FastAPI and managing event-driven microservices on Kafka. It requires expertise in LangChain, LangGraph, and Python async to deliver production-scale AI systems while mentoring junior engineers.
Skills required
About this role
Key Responsibilities
Leadership & Strategy
- Lead architectural design and implementation of multi-agent AI systems
- Drive technical strategy for GenAI initiatives and recommend best practices
- Mentor and provide technical guidance to junior and mid-level engineers
- Collaborate with stakeholders to define requirements and deliver solutions
- Own end-to-end delivery of complex, production-scale AI systems
Technical Execution
- Build and maintain high-performance REST/WebSocket APIs using FastAPI (Pydantic v2)
- Implement and optimize agentic AI systems using frameworks like LangGraph, Deep Agents, AutoGen, and LangChain
- Architect real-time, event-driven microservices using messaging queues like Apache Kafka
- Design clean, testable, maintainable services using SOLID principles, Python async, and type hints
- Integrate and optimize SQL, NoSQL, and vector databases (Postgres, MongoDB, ChromaDB, Pinecone)
- Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls, backed by LLM observability and evaluation tooling (e.g., LangSmith, Langfuse)
- Apply LLM safety guardrails (prompt-injection mitigation, PII handling, content moderation) in line with Banking, Insurance, and Healthcare compliance requirements
- Stay current with emerging trends in GenAI, deep learning, and AI orchestration framework
Skills and Competencies
- Proven ability to architect and deliver end-to-end GenAI solutions and multi-agent systems
- Strong software engineering discipline: testing (unit, integration, performance), code review, documentation
- Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders
- Strategic thinking and problem-solving with a focus on scalability and maintainability
-
Leadership capability: mentoring, technical guidance, and cross-functional collaboration
Responsibilities
Key Responsibilities
Leadership & Strategy
- Lead architectural design and implementation of multi-agent AI systems
- Drive technical strategy for GenAI initiatives and recommend best practices
- Mentor and provide technical guidance to junior and mid-level engineers
- Collaborate with stakeholders to define requirements and deliver solutions
- Own end-to-end delivery of complex, production-scale AI systems
Technical Execution
- Build and maintain high-performance REST/WebSocket APIs using FastAPI (Pydantic v2)
- Implement and optimize agentic AI systems using frameworks like LangGraph, Deep Agents, AutoGen, and LangChain
- Architect real-time, event-driven microservices using messaging queues like Apache Kafka
- Design clean, testable, maintainable services using SOLID principles, Python async, and type hints
- Integrate and optimize SQL, NoSQL, and vector databases (Postgres, MongoDB, ChromaDB, Pinecone)
- Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls, backed by LLM observability and evaluation tooling (e.g., LangSmith, Langfuse)
- Apply LLM safety guardrails (prompt-injection mitigation, PII handling, content moderation) in line with Banking, Insurance, and Healthcare compliance requirements
- Stay current with emerging trends in GenAI, deep learning, and AI orchestration framework
Skills and Competencies
- Proven ability to architect and deliver end-to-end GenAI solutions and multi-agent systems
- Strong software engineering discipline: testing (unit, integration, performance), code review, documentation
- Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders
- Strategic thinking and problem-solving with a focus on scalability and maintainability
-
Leadership capability: mentoring, technical guidance, and cross-functional collaboration
Qualifications
Minimum Qualifications
- Bachelor's degree in Computer Science, Data Science, or related field
- 5+ years of total professional experience, including:
- 3+ years of hands-on Software Engineering experience in Python, FastAPI and relevant tech stack
- 2+ years working specifically with GenAI and LLMs (GPT, Claude, LLaMA, etc.)
- Track record of shipping production ML/AI products, with strong prompt-engineering skills, systems-level thinking, and the ability to diagnose and resolve production failures
- Strong software engineering background with expertise in OOP and SOLID principles
- Proficiency in Python 3.11+ (async/await, type hints, modern Python patterns, strict type checking)
- Experience with agentic frameworks (LangChain, LangGraph, AutoGen, or similar)
- Proven track record building production REST/WebSocket APIs and microservices
- Experience with message streaming platforms (Kafka, Pulsar, or similar)
- Strong knowledge of databases: SQL, NoSQL, and vector databases
- Working knowledge of RAG pipelines (embeddings, chunking, retrieval) and LLM observability/evaluation tools (LangSmith, Langfuse, or similar)
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