Job Responsibilities:

- Design and build state-of-the-art AI agents using modern orchestration frameworks (e.g., LangGraph, LangChain, Google ADK) to automate complex reasoning tasks.
- Design and implement supervised and unsupervised machine learning models (e.g., regression, classification, clustering) and statistical experiments to support data-driven decision-making.
- Develop advanced RAG (Retrieval-Augmented Generation) pipelines and API connectors to ingest and synthesize data from diverse sources, including internal databases, unstructured technical documents, and external third-party data.
- Implement robust safety layers and input/output validation using specialized frameworks to prevent hallucinations, ensure data privacy, and maintain compliance.
- Build comprehensive monitoring pipelines using advanced evaluation tools (e.g., Arize Phoenix, Langfuse) to trace agent reasoning steps, track token usage, and monitor latency in production.
- Adopt rigorous evaluation frameworks (e.g., Ragas, GenAI Evaluation Service) to measure performance. Stay updated on research papers and cutting-edge algorithms in both the GenAI and ML domains.
- Develop and deploy AI solutions exclusively within the Cloud Platform (GCP/AWS/Azure) ecosystem, utilizing Vertex AI, Cloud Run, and BigQuery.

Qualifications

Qualifications:

- 3+ years of professional experience in Data Science or Software Engineering, with a strong dual focus on Generative AI/LLM applications and traditional Machine Learning.
- Bachelors or Masters degree in a quantitative field (e.g., Computer Science, AI, Statistics, Mathematics, or Engineering). A PhD is preferred.




- 2+ years of hands-on experience with supervised/unsupervised learning and statistical modeling.
- Proven experience building Agentic workflows (reasoning loops, tool use/function calling) rather than simple chatbots.
- Deep understanding of the GCP stack for AI/Data (Vertex AI, BigQuery).

Technical Skills:

- Expert proficiency in Python for building autonomous agents and model interaction (e.g., LangGraph, LangChain, Google ADK).
- Advanced proficiency in Python libraries such as Scikit-learn, NumPy, Pandas, Matplotlib, TensorFlow, or PyTorch.
- Skilled in integrating diverse data sources via SQL, Vector Databases, and APIs. Experience with data augmentation and efficient loading techniques.
- Proficiency in observability frameworks, guardrail implementation, and the Model Context Protocol (MCP).
- Deep experience with GCP services, specifically Vertex AI, Cloud Run, and BigQuery for deploying scalable AI solutions.

Functional Skills:

- Ability to decompose complex business challenges into executable AI agent workflows and technical specifications.
- Excellent verbal and written communication skills, with a demonstrated ability to translate complex technical information into easy, understandable language for non-technical audiences.
- Strong skills in building relationships and collaborating effectively with stakeholders to contribute to data-driven decision-making.

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📌 AI Engineer (Chennai)
🏢 Ford
📍 Chennai