Full Stack ML Engineer - Python/TypeScript in Bangalore, भारत

Job Title: SDE II - Full Stack ML Engineer (Python / TypeScript)Location: Hyderabad / Bangalore (On-site / Hybrid)Experience: 3 6 YearsCompensation: Competitive / As per industry standardsEmployment Type: Full-TimeAbout the Role:We are seeking an experienced Full Stack SDE II to bridge the gap between data science and user-facing applications. In this role, you will build scalable backends, design intuitive frontends, and integrate cutting-edge Machine Learning and AI models into production https://jobeax.com/link/D97KR0fNczjlIZqw Responsibilities:- Architect and develop robust backend systems and APIs using Python (FastAPI, Django, or Flask).- Build responsive, dynamic user interfaces and scalable middleware using TypeScript (https://jobeax.com/link/6WcBICn0LM558SP0, https://jobeax.com/link/Z30sZYC3jcRenVso, or https://jobeax.com/link/JSd7UYCSg572jJDY).- Integrate Machine Learning models (e.g., predictive analytics, NLP, LLMs) into production applications, ensuring low latency and high throughput.- Collaborate closely with Data Scientists to operationalize AI pipelines and optimize ML model inference.- Design and manage database architectures across both SQL (PostgreSQL) and NoSQL (MongoDB, Vector databases).- Deploy, scale, and monitor full-stack ML applications using cloud infrastructure (AWS/GCP), Docker, and CI/CD pipelines.Must-Have Qualifications:- 3 to 6 years of production-level software engineering experience.- Strong proficiency in Python for backend development and API design.- Deep hands-on experience with TypeScript and modern frontend frameworks (React/Next.js).- Proven experience integrating Machine Learning models into live software applications (MLOps, model serving, or AI API integrations).- Solid understanding of relational databases and system design principles.- Familiarity with cloud platforms (AWS) and containerization (Docker).Good-to-Have Skills:- Hands-on experience with Vector Databases (Pinecone, Weaviate) or RAG architectures.- Experience with ML serving frameworks like Triton, Ray Serve, or ONNX.- Background in building AI agents or working directly with LLM workflows. (ref:hirist.tech)