Data Scientist

EXL

. Lead architectural design and implementation of multi-agent AI systems .

Posted 9/29/2026full-timeNoida • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and implementing multi-agent AI systems, with a strong focus on Python, FastAPI, and GenAI technologies. Proven ability to mentor engineers, drive technical strategy, and ensure compliance with industry standards in Banking, Insurance, and Healthcare.

Highest-signal resume keywords

Multi-Agent AI Systems ArchitecturePython 3.11+ ProficiencyGenAI and LLM ExperienceREST/WebSocket API DevelopmentOOP and SOLID Principles Expertise

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonFastAPILangChainLangGraphAutoGenApache KafkaSQL DatabasesNoSQL DatabasesVector DatabasesPrompt Engineering

Soft Skills

Excellent CommunicationStrategic ThinkingProblem-SolvingMentoringCross-Functional Collaboration

Tools & Technologies

Pydantic v2Deep AgentsLangSmithLangfuseHuman-in-the-Loop Controls

Certifications & Qualifications

Bachelor's Degree in Computer ScienceBachelor's Degree in Data Science

Industry Keywords

Banking ComplianceInsurance ComplianceHealthcare ComplianceML/AI ProductsEvent-Driven Microservices

Tech Stack

Tools & technologies

ApacheKafkaMicroservicesMongoDBNoSQLPostgresPulsarPythonSQL

About the role

Key responsibilities & impact

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
Build and maintain high-performance REST/WebSocket APIs using FastAPI and Pydantic v2
Implement and optimize agentic AI systems using LangGraph, Deep Agents, AutoGen, and LangChain
Architect real-time, event-driven microservices using messaging queues such as Apache Kafka
Design clean, testable, maintainable services using SOLID principles, Python async, and type hints
Integrate and optimize SQL, NoSQL, and vector databases including Postgres, MongoDB, ChromaDB, and Pinecone
Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls
Use LLM observability and evaluation tooling such as LangSmith and Langfuse
Apply LLM safety guardrails including prompt-injection mitigation, PII handling, and content moderation
Stay current with emerging trends in GenAI, deep learning, and AI orchestration frameworks
Apply practices aligned with Banking, Insurance, and Healthcare compliance requirements

Requirements

What you’ll need

Bachelor's degree in Computer Science, Data Science, or related field
5+ years of total professional experience
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
Strong prompt-engineering skills and systems-level thinking
Ability to diagnose and resolve production failures
Expertise in OOP and SOLID principles
Proficiency in Python 3.11+ including async/await, type hints, modern Python patterns, and strict type checking
Experience with agentic frameworks such as LangChain, LangGraph, and AutoGen
Proven track record building production REST/WebSocket APIs and microservices
Experience with message streaming platforms such as Kafka or Pulsar
Strong knowledge of SQL, NoSQL, and vector databases
Working knowledge of RAG pipelines and LLM observability/evaluation tools
Excellent communication skills and ability to explain complex technical concepts to non-technical stakeholders
Strategic thinking and problem-solving focused on scalability and maintainability
Leadership capability including mentoring, technical guidance, and cross-functional collaboration