Sr Engineer (AI/Backend)

Key Responsibilities Core Engineering

• Advanced Python skills with a strong grasp of clean, modular, and maintainable code practices
• Experience building production-ready backend services using frameworks like FastAPI, Flask, or Django
• Strong understanding of software architecture, including RESTful API design, modularity, testing, and versioning.
• Experience working with databases (SQL/NoSQL), caching layers, and background job queues.

AI/ML & GenAI Expertise

• Hands-on experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment
• Practical experience with LLMs and GenAI tools such as OpenAI APIs, Hugging Face, LangChain, or Transformers
• Understanding of how to integrate LLMs into applications through prompt engineering, retrieval-augmented generation (RAG), and vector search
• Comfortable working with unstructured data (text, images) in real-world product environments
• Bonus: experience with model fine-tuning, evaluation metrics, or vector databases like FAISS, Pinecone, or Weaviate

Ownership & Execution

• Demonstrated ability to take full ownership of features or modules from architecture to delivery
• Able to work independently in ambiguous situations and drive solutions with minimal guidance
• Experience collaborating cross-functionally with designers, PMs, and other engineers to deliver user-focused solutions
• Strong debugging, systems thinking, and decision-making skills with an eye toward scalability and performance

Required Qualifications

• Bachelor’s in Computer Science, Engineering, Data Science, or a related field
• 3–7 years of experience in building and deploying ML models in real-world applications
• Proficient in Python and ML libraries/frameworks like TensorFlow, PyTorch, scikit-learn, etc.
• Solid understanding of algorithms in supervised, unsupervised, and deep learning
• Experience with data preprocessing, feature engineering, and model evaluation
• Familiarity with cloud platforms (AWS, GCP, or Azure) and ML Ops practices
• Strong problem-solving and leadership skills with the ability to take ownership

Nice To Have

• Experience with NLP, computer vision, or time-series analysis
• Knowledge of containerization (Docker, Kubernetes) and model deployment pipelines
• Contributions to open-source projects or published research
• Experience managing a team or leading cross-functional projects

Skills: scikit-learn,nlp,tensorflow,docker,ml ops,python,pytorch,kubernetes