Ml Engineer (Classical Ml + Genai)

ML Engineer (Classical ML + GenAI) Location - Hyderabad Experience - 4+ years 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 ML Engineer (Classical ML + GenAI) Location - Hyderabad Experience - 4+ years 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