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