AI Engineer

Job Description

Role Overview

We are looking for a deeply hands-on AI professional to design, build, and deploy enterprise-grade AI solutions across the Manufacturing & Quality value chain. The role spans Generative AI, applied AI engineering, and enterprise data & knowledge platforms, with end-to-end ownership from architecture to production. 

This is a builder role. Screening will emphasize demonstrable, hands-on delivery of production Generative AI systems not slideware or PoCs.

Key Responsibilities

AI Solution Design & Delivery

• Design and develop end-to-end Generative AI applications for enterprise use cases such as document intelligence, knowledge assistants, and workflow automation.
• Build production-grade RAG pipelines with strong grounding, evaluation, and observability.
• Develop agentic AI workflows and multi-agent systems for complex reasoning and orchestration tasks.
• Build multimodal AI solutions capable of processing text, PDFs, tables, images, and scanned documents.

Architecture & Engineering

• Own end-to-end solution architecture: data ingestion, model layer, orchestration, APIs, evaluation, and deployment.
• Build scalable backend APIs and microservices in Python (FastAPI) with clean, testable, well-documented code.
• Deploy and maintain AI solutions on cloud-native infrastructure (GCP preferred; AWS/Azure acceptable) using containers and CI/CD.
• Implement MLOps/LLMOps practices versioning, monitoring, cost governance, and rollback safety.

Evaluation, Governance & Compliance

• Design AI evaluation frameworks covering accuracy, groundedness, faithfulness, hallucination control, and bias.
• Instrument Human-in-the-Loop (HITL) gates and audit-ready logs suitable for regulated environments.
• Align outputs with data integrity, GxP, and enterprise security requirements.

Collaboration, Ownership & Innovation

• Partner with the Principal Product Manager, Principal Data Scientist, business SMEs, and cloud partners to translate requirements into production-ready systems.
• Assimilate detailed project skill files, PRDs, and technical specs shared for different initiatives, and rapidly convert them into working solutions.
• Bring fresh ideas and challenge existing designs with data-backed recommendations.
• Mentor junior team members and interns; contribute to internal AI best practices.

Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field from a reputed institute.

Experience

• 4 to 7 years overall in AI Engineering / Data Science / ML.
• At least 2 to 3 years of hands-on, production-grade LLM / Generative AI development building, deploying, and maintaining real systems (not prototypes).
• Remaining experience in strong software engineering or applied ML delivery is acceptable.

Must-Have Skills (Hands-on, Production-grade)

Generative AI

• Hands-on experience with LLMs such as GPT, Gemini, or Anthropic models, and transformer-based architectures.
• Retrieval-Augmented Generation (RAG) chunking strategies, embeddings, vector stores, and re-ranking, deployed in production.
• Prompt engineering and prompt versioning discipline.
• LLM evaluation frameworks and metrics (groundedness, faithfulness, coverage, hallucination).

Programming & Engineering

• Python (Advanced) production code, testing, packaging, and performance tuning.
• SQL analytical queries on modern warehouses.
• FastAPI (or Django) for backend service development.
• Firebase (or equivalent), Git, REST APIs, Docker, CI/CD basics, and Linux.

Cloud & Delivery

• Production experience on at least one of GCP, AWS, or Azure; comfort with IAM, storage, compute, and managed AI services.
• Strong software engineering and system design skills, with proof of taking AI systems from prototype to production with defined SLAs.

Good-to-Have Skills

Advanced AI / GenAI

• Building agents on existing agent platforms (e.g., Vertex AI Agent Builder, Copilot Studio, LangGraph, CrewAI, or equivalent).
• Multi-agent orchestration for complex enterprise workflows.
• Multimodal AI (text + images + tables + scanned documents) and document AI (OCR, layout models, hierarchical extraction).
• Fine-tuning, distillation, or adapter-based customization of LLMs.

Enterprise Data & Knowledge Platforms

• Exposure to designing or contributing to enterprise data & knowledge platforms — canonical data models, semantic layers, or knowledge graphs that power multiple downstream AI applications.
• Familiarity with graph databases (Neo4j, AuraDB, or similar) and knowledge-graph patterns.
• Modern data stack experience: BigQuery, dbt, and workflow orchestrators such as Airflow or Cloud Composer.