Machine Learning Engineer

Exp range - 4 to 6 Years

Interview Process - Screening >> AI Interview >> L1 Discussion

Tittle- Senior Machine Learning Engineer

Responsibilities

Generative AI & agentic systems

• Design and implement generative AI applications including RAG systems, agentic workflows, and multi-agent orchestration for complex business problems
• Build agentic systems combining memory, planning, and dynamic reasoning for multi-step problem-solving across enterprise datasets
• Develop multi-agent architectures using modern orchestration frameworks with reliable communication and observability
• Implement prompt engineering, context optimization, and evaluation frameworks for GenAI applications

Traditional ML & computer vision

• Design and implement ML pipelines for forecasting, recommendations, classification, and regression problems at scale
• Build production computer vision systems for document understanding, image analysis, and visual enterprise applications
• Develop feature engineering strategies and statistical models; optimize models for production using hyperparameter tuning and performance benchmarking

MLOps & production engineering

• Own the complete ML lifecycle: CI/CD pipelines, automated testing, model versioning, validation gates, and progressive deployment
• Build production APIs and microservices with authentication, error handling, and monitoring; design data pipelines and integrations
• Monitor production ML systems, track model drift, maintain system reliability, and implement A/B testing frameworks

Knowledge solutions

• Architect knowledge graph and semantic search solutions enabling entity resolution, relationship discovery, and intelligent retrieval
• Design hybrid retrieval combining vector embeddings with keyword search

Client collaboration

• Present technical solutions to clients, translating engineering decisions into business outcomes
• Collaborate with architects, data engineers, and business analysts on integrated solutions

Required qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent demonstrated experience)
• 3–6 years of hands-on ML engineering with demonstrated expertise across multiple domains (GenAI, traditional ML, computer vision)
• Expert-level Python proficiency with strong software engineering fundamentals: API design, testing, containerization
• Proven track record shipping production ML systems in cloud environments with GCP (Vertex AI, BigQuery, Cloud Run) or equivalent
• Experience building GenAI, traditional ML, and computer vision applications; MLOps practices; retrieval-augmented generation

Preferred qualifications

• Google Cloud Professional Machine Learning Engineer certification
• Knowledge graph and semantic search implementations; regulated industry experience (Healthcare, Financial Services)
• Published technical content or open-source contributions