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AI/ML Engineer

Ford Motor Company

Chennai

3+ years

1 day ago

$22.9K–36.1K/yr

Full-time

Hybrid

Skills Required

LLM

RAG

Gen AI

Agentic Systems

Transformer architectures

LangChain

LlamaIndex

Machine Learning

MLOps

Python

PyTorch

TensorFlow

Scikit-learn

Deep Learning

NLP

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.

Role: Senior AI/ML Engineer

Location: Chennai, India | Hybrid

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

Qualification

- 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-workplace 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

- Category: Enterprise Technology

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