SDE II - Full Stack ML Engineer (Python / TypeScript)

Job Title: SDE II - Full Stack ML Engineer (Python / TypeScript)

Location: Hyderabad / Bangalore / Gurgaon (On-site) 

Experience: 3 – 6 Years

Compensation: Competitive / As per industry standards

Employment Type: Full-Time

About 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 environments.

Key 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 (React.js, Next.js, or Node.js).

• 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.