AI Engineer

Key Responsibilities

• Design and implement RAG pipelines end-to-end: ingestion chunking embedding retrieval reranking grounding and evaluation for knowledge-intensive applications.
  

• Design and implement end-to-end image generation pipelines using Hugging Face models and the Diffusers ecosystem covering dataset/input preparation prompt/workflow orchestration inference serving safety/quality checks and production monitoring.
  

• Build autonomous/agentic AI workflows using LangGraph and frameworks like LangChain (tool calling routing state/memory multi-step orchestration guardrails).
  

• Develop and maintain Python REST APIs for AI services using FastAPI including request/response schemas validation error handling API versioning and documentation.
  

• Design train and validate supervised and unsupervised ML models for business use-cases (classification regression ranking).
  

• Productionize models: containerize ML artifacts/services with Docker define CI/CD pipelines and deploy services to cloud-based infrastructure.
  

• Build and maintain reliable data ingestion and feature engineering pipelines using SQL and Python data-processing libraries.
  

• Implement model monitoring and quality controls: evaluation metrics offline/online testing A/B testing drift detection latency/cost monitoring and rollback strategies.
  

• Integrate AI/ML services into backend systems; collaborate with engineers and product owners to deliver end-to-end solutions.
  

• Work on computer vision pipelines: preprocessing augmentation and transformation using OpenCV and PIL; integrate CV modules into ML workflows.
  

• Document experiments maintain reproducible workflows and contribute to engineering best practices and knowledge sharing.
  

Must-Have Skills & Qualifications

• 5 years of professional experience building and deploying ML/AI solutions.
  

• Strong programming skills in Python with solid software engineering practices.
  

• Strong applied statistics and ML fundamentals (evaluation regularization feature engineering error analysis).
  

• Strong SQL skills and experience building data/feature pipelines.
  

• Experience with LLMs and NLP techniques for enterprise applications (prompting embeddings RAG patterns evaluation).
  

• Strong experience building REST APIs using FastAPI.
  

• Experience with containerization using Docker.
  

• Excellent communication skills. Ability to clearly explain technical concepts to non-technical stakeholders write clean documentation and collaborate effectively across engineering data and product teams.
  

Preferred Skills

• Experience with AWS (or Azure/GCP) and deploying inference services in cloud environments.
  

• Kubernetes for orchestration autoscaling and production deployments.
  

• MLOps practices: CI/CD for ML experiment tracking model registry automated evaluation monitoring and governance.
  

• Experience with agentic frameworks: LangGraph LangChain AutoGen (or similar) including tool integration and multi-agent orchestration.
  

• Experience with RAG infrastructure components (vector databases embedding models rerankers) and evaluation strategies.
  

• Inference optimization (batching caching concurrency tuning) and cost-performance trade-offs.
  

• Familiarity with enterprise security/compliance (PII handling access controls auditability).
  

Nice-to-Have

• Practical knowledge of OpenCV and PIL for image processing integrated into ML pipelines.
  

• Experience implementing image generation workflows using Stable Diffusion and FLUX (pipelines prompt engineering safety filtering evaluation).
  

• Experience building multimodal applications (text image) and deploying them as reliable services.
  

• GenAI testing and evaluation experience: building/using evaluation harnesses for RAG/agentic systems defining quality metrics regression testing prompts/workflows and monitoring quality drift in production.