ML Engineer

Location: Hyderabad, Hybrid
Experience: 4+

This role delivers AI use cases from first principles to production. The profile spans classical ML (predictive modelling, process optimisation) and GenAI (LLM-powered applications, RAG, agents) not a specialist in one, but genuinely capable across both. The defining quality is the ability to take a business problem, select the right technical approach, and see it through to a reliable, deployed product not just a proof of concept.

CORE RESPONSIBILITIES

• Own the full technical lifecycle of AI use cases: problem and mvp scoping → data analysis → model/application development → pilot → productionisation
• Build GenAI applications: RAG pipelines, LLM-powered features, agents, and prompt orchestration workflows for classical and more manufacturing related use cases
• Build and productionise classical ML and Deep Learning models for manufacturing use cases (e.g., predictive maintenance, smart allocation, predictive DFM )
• Evaluate and iterate define success metrics, run experiments, measure model and application performance in production

KEY SKILLS

• Classical ML and Deep Learning experience
• GenAI: LLM APIs,RAG patterns, LangChain / LlamaIndex, fine-tuning, prompt engineering at scale
• Data: can prepare their own datasets, experience in data processing for structured and highly unstructured data
• Productionisation: writing clean, testable code; working with Docker; understanding how their models will be served
• Evaluation mindset: knows how to define and measure quality for both ML models and GenAI applications

WHAT GOOD LOOKS LIKE

• Has taken at least one classical ML use case, one Deep Learning and one complex GenAI use case from prototype to production
• Can write production-quality code and understands what it takes to deploy reliably
• Comfortable with ambiguity in problem definition — can scope progressive MVP scopes to allow early value
• Good engineering instincts: doesn’t over-engineer, but doesn’t produce fragile one-off scripts either
• Would be great if the candidate has some experience in applying AI to complex engineering data (e.g., 3D geometries, complex documents)

WHAT THIS ROLE IS NOT

• Not a pure research scientist the bar is production delivery, not publication
• Not an LLM specialist only classical ML and DL use cases are equally in scope and require genuine capability on diverse data and use cases