AI/ML Engineer

Top Gen AI Jobs

Chennai District

Hybrid

INR 2,191,000 - 3,455,000

Full time

33 hours ago

Be an early applicant

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Hybrid work model

Job summary

Ford Motor Company in Chennai, India is seeking a Senior AI/ML Engineer specialized in Generative AI and Agentic Systems. You will design and deploy autonomous AI agents and RAG pipelines in production, working with GCP, Docker, and Kubernetes environments.

The role requires 3+ years of experience in AI/ML, strong Python skills, and hands-on work with LLMs, LangChain, and vector databases. This hybrid position offers collaboration with enterprise teams and responsible AI practices.

Qualifications

3+ years of professional AI/ML experience in a relevant field.
Hands-on design of LLM-based or generative AI systems in production.
Expert-level Python programming skills.
Experience with GCP and containerized environments.
Experience with vector indexing and semantic search.
Familiarity with CI/CD tooling and IaC practices.

Responsibilities

End-to-end design, development, optimization, and deployment of autonomous and semi-autonomous AI agents.
Architect multi-agent orchestration systems and enterprise-grade RAG pipelines.
Implement production-ready MLOps infrastructure on GCP and in containerized environments.
Develop agents to automate and optimize enterprise business processes.
Build robust orchestration patterns, task decomposition methods, cognitive planning loops, and inter-agent communication protocols.
Integrate agents with external enterprise systems, APIs, databases, and Google Cloud services.
Implement memory management across user sessions and maintain prompt libraries.

Skills

LLM

RAG

Gen AI

Agentic Systems

Transformer architectures

LangChain

LlamaIndex

Machine Learning

MLOps

Python

PyTorch

TensorFlow

Scikit-learn

Deep Learning

NLP

Education

GCP Professional Machine Learning Engineer certification

Google Professional Cloud Architect certification

Tools

Docker

Kubernetes/GKE

Cloud Run

Cloud Functions

Terraform

Git

Job description

Job Details

Company: Ford Motor Company

Title: AI/ML Engineer

Location: Chennai, India | Hybrid

Experience: 3+ years

Posted: 1 day ago

Salary: $22.9K–36.1K/yr

Employment Type: Full-time

Work Arrangement: Hybrid

Description

Seeking an experienced Senior AI/ML Engineer specialized in Generative AI and Agentic Systems. The role centers on building production-grade autonomous and semi-autonomous AI solutions.

Skills Required

Gen AI

Agentic Systems

Transformer architectures

LangChain

LlamaIndex

Machine Learning

MLOps

Python

PyTorch

TensorFlow

Scikit-learn

Deep Learning

Experience

Minimum 3 years of professional experience in Machine Learning or AI Engineering

1 to 2 years of hands-on experience designing and implementing LLM-based, generative AI, or agentic systems in production

Expert-level Python programming skills

Experience with Google Cloud Platform and container services

Experience with vector indexing and semantic search

Experience with Terraform, Git, and automated CI/CD tools

Experience setting up monitoring solutions and evaluating LLM outputs for quality and safety

Qualifications

GCP Professional Machine Learning Engineer certification

Google Professional Cloud Architect certification

Responsibilities

End-to-end design, development, optimization, and deployment of autonomous and semi-autonomous AI agents

Architect multi-agent orchestration systems and enterprise-grade RAG pipelines

Implement production-ready MLOps infrastructure on GCP and in containerized environments

Develop agents to automate and optimize enterprise business processes

Build robust orchestration patterns, task decomposition methods, cognitive planning loops, and inter-agent communication protocols

Build, secure, and maintain integrations between AI agents and external enterprise systems, APIs, databases, and Google Cloud services

Implement advanced memory management across user sessions

Author, test, and optimize prompt templates and maintain reusable prompt libraries

Evaluate and fine-tune foundation models to balance capability, latency, and inference cost

Design high-throughput, low-latency RAG pipelines with clean ingestion, chunking, and embedding generation

Integrate scalable vector databases to ground responses in verified enterprise knowledge

Analyze, clean, and pre-process structured and unstructured data sources

Write, test, and maintain Infrastructure as Code for secure GCP environments

Build and maintain CI/CD pipelines for testing, container building, and multi-environment deployment

Package code, agents, and dependencies into secure Docker containers

Configure and maintain workflow orchestrators for evaluation, fine-tuning, and ingestion automation

Establish evaluation metrics and testing pipelines for task completion, reasoning, tool calling, latency, token use, and hallucinations

Implement input/output filtering, moderation, grounding validation, prompt-injection defenses, privacy checks, and human approval gates

Implement production monitoring, logging, tracing, and alerting

Audit and optimize workflows, model parameters, caching, and infrastructure for cost-efficiency and performance

Adhere to version control standards for code, prompts, Dockerfiles, and Terraform

Partner with Data Scientists, Software Engineers, and business units

Maintain technical documentation including architecture diagrams, flowcharts, tool definitions, prompt strategies, containerization guidelines, and SOPs

Nice To Have

Use of Google Cloud services such as Vertex AI Model Garden, Cloud Run, Cloud Functions, Cloud Monitoring, Cloud Logging, and Cloud Trace

Use of LLM observability tools like LangSmith

Use of evaluation tools from Vertex AI

Use of Docker, Kubernetes/GKE, Cloud Run, and Cloud Functions

Use of AlloyDB or equivalent vector stores

More Skills

LangGraph, CrewAI, Vertex AI Agent Builder, Google Cloud Platform, Docker, Kubernetes, GKE, Cloud Run, Cloud Functions, BigQuery, Vertex AI, Vertex AI APIs, Vertex AI Model Garden, Vertex AI Vector Search, AlloyDB, REST, GraphQL, SQL, NoSQL, Terraform, Git, Cloud Build, GitLab CI, Jenkins, Vertex AI Pipelines, Cloud Composer, Airflow, Prometheus, Grafana, Cloud Monitoring, Cloud Logging, Cloud Trace, LangSmith, Model Context Protocol, function-calling, structured tool execution, Gemini, LLM-as-a-judge

Other