Senior AI Engineer

Tata Consultancy Services Limited
Irvine, CA, United States
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Compensation
$90,000.0 - $150,000.0
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Continuous Integration Data Architecture Information Engineering Data Governance DevOps Distributed Computing Environment Distributed Systems Amazon DynamoDB
+24 more
Elasticsearch Graph Database Identity and Access Management Python (Programming Language) Machine Learning Modular Design Node.Js Redis Search Technologies Software Engineering Amazon ElastiCache Large Language Models Multi-Agent Systems Prompt Engineering Apache Spark Spring-boot Backend Kubernetes Infrastructure Automation Frameworks Deployment Automation Cloudwatch Docker Databricks Microservices

Requirements

Do you have experience in Spark?, * 5+ years of experience in software engineering, data engineering, or AI/ML engineering

  • Strong proficiency in Python for AI/data workflows and automation
  • Hands-on experience building solutions in AWS cloud environments
  • Experience with:

  • Databricks (or similar) and Apache Spark for distributed data processing
  • OpenSearch / Elasticsearch (including vector search)
  • Graph databases (Neptune or similar)
  • DynamoDB and Redis/ElastiCache

Experience building backend services and APIs (e.g., Java/Spring Boot, Node.js)

Production experience with Docker and Kubernetes

Experience with CI/CD pipelines and deployment automation

Strong understanding of distributed systems, data architecture, and scalable design, * Experience with LLM/GenAI architectures (RAG, embeddings, prompt engineering)

  • Familiarity with LangGraph, AutoGen, CrewAI, or similar agent orchestration frameworks
  • Experience with LangChain or LlamaIndex
  • Experience implementing LLM evaluation and observability frameworks
  • Familiarity with AI security practices and threat models (prompt injection, guardrails)
  • Experience working in regulated environments with strong data governance and compliance requirements

Tech Stack

  • AWS: Neptune, OpenSearch, DynamoDB, ElastiCache (Redis), IAM, CloudWatch
  • Data: Databricks, Apache Spark
  • AI: LLM integrations, embeddings, vector search, RAG pipelines
  • Agentic/LLM Tooling: LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI
  • Backend: APIs, microservices (e.g., Spring Boot, Node.js)
  • DevOps: Docker, Kubernetes, CI/CD, Infrastructure as Code

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