Primary Responsibilities
- Design, develop, and deploy production-ready AI/ML solutions for enterprise applications.
- Build and optimize Large Language Model (LLM) applications, AI agents, and Retrieval-Augmented Generation (RAG) pipelines.
- Fine-tune, evaluate, and optimize open-source and commercial AI models for specific business use cases.
- Develop scalable APIs and AI services using Python frameworks such as FastAPI or Flask.
- Build intelligent automation workflows using AI, vector databases, and orchestration frameworks.
- Design and implement machine learning pipelines for data preprocessing, feature engineering, model training, validation, and deployment.
- Collaborate with Product Managers, Architects, UI/UX Designers, and DevOps teams to deliver AI-powered products.
- Optimize model inference performance, latency, and infrastructure costs.
- Mentor junior AI engineers and participate in architecture discussions, technical reviews, and code reviews.
- Research and evaluate emerging AI technologies and recommend adoption where appropriate.
- Ensure AI applications follow security, privacy, and responsible AI best practices.
Technical RequirementsProgramming Languages
- Strong expertise in Python.
- Good knowledge of SQL.
- Basic understanding of JavaScript or TypeScript for AI integrations.
Artificial Intelligence & Machine Learning
- Strong experience with Machine Learning, Deep Learning, NLP, and Generative AI.
- Hands-on experience with LLMs such as GPT, Claude, Llama, Qwen, Mistral, or Gemma.
- Experience developing AI Agents and multi-agent workflows.
- Strong understanding of prompt engineering, structured outputs, function calling, and tool integration.
- Experience implementing Retrieval-Augmented Generation (RAG) architectures.
- Knowledge of embedding models and semantic search.
AI Frameworks (Any)
- LangChain
- LangGraph
- LlamaIndex
- Haystack (preferred)
- OpenAI SDK
- Hugging Face Transformers
- Sentence Transformers
Machine Learning Frameworks (Any)
- PyTorch
- TensorFlow
- Scikit-learn
- XGBoost
- LightGBM
Vector Databases (Any)
- Pinecone
- Qdrant
- Milvus
- ChromaDB
- FAISS
Backend Development
- FastAPI
- Flask
- RESTful APIs
- WebSockets
- Background workers (Celery/RQ)
Databases (Any)
Cloud & Infrastructure (Any)
- AWS, Azure, or Google Cloud Platform
- Docker
- Kubernetes (preferred)
- Git
- CI/CD pipelines
- Linux environments
MLOps (Any)
- MLflow
- Weights & Biases
- Model versioning
- Model deployment
- Monitoring and observability
- GPU optimization and inference serving
Computer Vision (Preferred) (Any)
- OpenCV
- YOLO
- OCR
- Image classification
- Object detection
- Pose estimation
Voice AI (Preferred)
- Speech-to-Text (STT)
- Text-to-Speech (TTS)
- Real-time voice assistants
- Voice activity detection
- Audio streaming
Additional Skills
- Strong knowledge of software architecture and design patterns.
- Experience building scalable AI SaaS platforms.
- Understanding of API security, authentication, and data privacy.
- Excellent debugging and performance optimization skills.
- Familiarity with Agile/Scrum development methodology.
Experience
- Minimum Years of Experience: 5+ Years
- Relevant Industry Experience: Minimum 5 years of hands-on experience in Artificial Intelligence, Machine Learning, and Deep Learning, including 2+ years of experience building production-ready Generative AI and LLM-based applications.
Apply : [email protected]
Work Location: In person
