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
Backend & API Development
- 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
AI Integration & Tools
- 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
Data & Database Engineering
- 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
AI Services & Architecture
- 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
MLOps & Model Handling (Optional)
- 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
Required Skills
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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
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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
Good-to-Have Skills
- 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)
Educational Background
- Bachelor s or Master s in Computer Science, Data Science, or related fields
- Certifications in Python, AI/ML, or Cloud AI are advantageous
What You ll Build
- 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
