Ai Ml Engineer

Role & responsibilities

1. Backend & API Development

• Build and maintain high-performance FastAPI/Flask microservices for AI-driven applications.
• Integrate AI/ML models into backend APIs for chatbots, RAG systems, or recommendation flows.
• Implement secure RESTful and event-driven APIs with proper versioning and error handling.
• Manage authentication, rate limiting, and audit logging for AI endpoints.

2. AI Integration & Tools

• Connect cloud AI APIs (OpenAI, Anthropic, Gemini, etc.) and self-hosted models (Ollama, vLLM).
• Develop AI SDK wrappers for text, image, video, and voice intelligence modules.
• Use LangChain, LlamaIndex, and embeddings for retrieval-augmented workflows.
• Implement pipelines for document parsing, summarization, and contextual reasoning.

3. Data & Database Engineering

• Build robust 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 use.

4. AI Services & Architecture

• Architect scalable, modular components for multi-modal 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.

5. MLOps & Model Handling

• Basic familiarity with model deployment using Docker, MLflow, or TorchServe.
• Support fine-tuning and inference workflows when required.
• Optional exposure to Vertex AI / SageMaker / KServe is a plus.

Preferred candidate profile

• Programming:

  Strong proficiency in Python, OOPs, and API design principles.

• Frameworks:

  FastAPI / Flask, understanding of PyTorch or TensorFlow.

• Databases:

  PostgreSQL, Redis, and vector databases (Pinecone, Weaviate, FOSS, pgvector).

• AI Tools:

  Experience with OpenAI, LangChain, LlamaIndex, HuggingFace, or similar APIs.

• Architecture:

  REST APIs, microservices, and containerized deployments.

• Infra:

  Working knowledge of Docker, basic Kubernetes/GPU familiarity preferred.
• Exposure to agentic frameworks like LangGraph, CrewAI, or AutoGen, MCP and A2A Protocols.

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, fine-tuning (LoRA, PEFT), or data validation tools.
• Awareness of data governance and observability (EvidentlyAI, Prometheus, Grafana).

Soft Skills

• Strong analytical reasoning and ownership mindset.
• Collaborative and agile work style across multi-functional teams.
• Ability to write clean, maintainable, production-ready code.
• Curiosity and self-drive to explore the evolving AI ecosystem.

Educational Background

• Bachelors 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 providers via unified SDKs.
• Scalable RAG and conversational agents connected to real-time data.
• Intelligent APIs enabling text, speech, and image-based AI experiences.