Job Description
We are seeking a Python-Focused AI/ML Engineer with strong backend engineering expertise to build and integrate intelligent systems into production applications. This role combines backend development, data pipelines, and applied AI integration — working with APIs, SDKs, and orchestration layers that connect cloud AI services and self-hosted models.
Key Responsibilities
- Develop and maintain high-performance FastAPI/Flask microservices for AI-driven products
- Integrate AI/ML models into backend APIs for chatbots, RAG systems, and recommendation engines
- Implement secure RESTful and event-driven APIs with versioning, error handling, and monitoring
- Manage authentication, rate limiting, and audit logging for AI endpoints
- Integrate cloud AI APIs (OpenAI, Anthropic, Gemini, Groq, etc.) and self-hosted models (Ollama, vLLM)
- Develop SDK wrappers for text, image, video, and voice-based intelligence modules
- Use LangChain, LlamaIndex, and embeddings for retrieval-augmented generation (RAG) workflows
- Implement pipelines for document parsing, summarization, and contextual reasoning
- Build data ingestion and transformation pipelines using Pandas, NumPy, or Airflow/Prefect
- Integrate and query vector databases (Pinecone, Weaviate, pgvector, Milvus) for embeddings
- Design schemas and optimize queries for PostgreSQL and NoSQL systems supporting AI workloads
- Handle structured/unstructured data (PDFs, audio, text) efficiently for downstream AI tasks
- Architect scalable, modular components for multimodal AI (text, speech, image)
- Build SDK-based AI services for unified orchestration across multiple providers
- Optimize backend-to-model communication for low latency and high throughput
- Collaborate with frontend and DevOps teams for full-stack integration
- Basic familiarity with model deployment using Docker, MLflow, or TorchServe
- Support fine-tuning and inference workflows when required
- Exposure to Vertex AI / SageMaker / KServe is a plus
- Programming & Development
- Strong proficiency in Python, OOP principles, and API design
- Experience with FastAPI or Flask microservices
- Understanding of PyTorch or TensorFlow frameworks
- Databases
- Proficiency in PostgreSQL, Redis, and vector databases like Pinecone, Weaviate, pgvector, or Milvus
- AI & Integration Tools
- Experience with LangChain, LlamaIndex, HuggingFace, OpenAI, or similar APIs
- Familiarity with FOSS AI stacks, embeddings, and agentic frameworks (LangGraph, CrewAI, AutoGen)
- Knowledge of MCP and A2A communication protocols
- Architecture & Infrastructure
- Understanding of REST APIs, microservices, and containerized deployments
- Working knowledge of Docker; basic Kubernetes/GPU familiarity preferred
- Soft Skills
- Strong analytical reasoning and ownership mindset
- Collaborative and agile work style across multi-functional teams
- Ability to write clean, maintainable, production-grade code
- Curiosity and self-drive to explore the evolving AI ecosystem
- Familiarity with speech, image, or document AI APIs (Whisper, DALL·E, Textract, Stable Diffusion)
- Experience integrating cloud AI providers (AWS Bedrock, GCP Vertex AI, Azure OpenAI)
- Knowledge of embedding optimization, LoRA/PEFT fine-tuning, and data validation tools
- Awareness of data governance and observability tools (EvidentlyAI, Prometheus, Grafana)
- Bachelor’s or Master’s in Computer Science, Data Science, or related fields
- Certifications in Python, AI/ML, or Cloud AI are advantageous
- AI-driven backend systems integrating multiple AI providers via unified SDKs
- Scalable RAG and conversational agents connected to real-time data
- Intelligent APIs enabling text, speech, and image-based AI experiences
Educational Qualifications
Bachelor’s degree in B.Tech/B.E