Key Responsibilities

- Design, develop, and optimize Python-based AI applications for Generative AI use cases.
- Build, fine-tune, and deploy LLMs (LLAMA, OpenAI, Mistral, etc.).
- Develop and standardize AI Copilot frameworks.
- Build Agentic AI systems with multi-agent reasoning, tool calling, and orchestration.
- Design and manage RAG pipelines using Qdrant DB, Chroma DB, and other Vector Databases.
- Implement Deep Learning models for image processing and multimodal AI.
- Perform image preprocessing, OCR, document AI, and vision model integration.
- Develop audio processing pipelines, including:
- Speech-to-Text (STT) using Whisper
- Audio preprocessing and noise handling
- Build real-time voice & AI interaction systems using WebRTC.
- Optimize workloads on AWS & Azure GPU instances, including GPU and memory management.
- Develop scalable ETL pipelines for data ingestion, embeddings, and training workflows.
- Deploy and maintain AI services in Linux-based environments.
- Collaborate with cross-functional teams to deliver production-ready AI systems.

Must-Have Skills

- Expert-level Python programming.
- Solid hands-on experience with Generative AI & LLMs.
- Experience working with LLAMA or equivalent models.
- Hands-on experience with Vector Databases:
- Qdrant DB
- Chroma DB
- FAISS, Pinecone, Milvus, or similar




- Strong understanding of GPU computing, memory optimization, and performance tuning.
- Experience with AWS GPU EC2 and Azure GPU services.
- Solid experience with Deep Learning frameworks (PyTorch, TensorFlow).
- Strong knowledge of Image Processing.
- Hands-on experience with Audio Processing, including Whisper.
- Experience building real-time audio pipelines using WebRTC.
- Proficiency in Linux environments.
- Working knowledge of SQL databases.

Good-to-Have (Bonus) Skills

- Experience with LLM orchestration frameworks:
- LangChain
- LlamaIndex
- Haystack
- Experience with Agentic AI frameworks.
- API development using FastAPI, Streamlit, or similar.
- Experience implementing CI/CD pipelines for AI/ML workloads.
- Knowledge of ETL pipelines and data engineering.
- Familiarity with multimodal LLMs (text + image + audio).
- Experience across AWS services (S3, API Gateway, EC2) and Azure services.
- Exposure to multiple GenAI models (OpenAI, LLAMA, Mistral, Google Bard, etc.).

Nice-to-Have

- Experience with real-time AI assistants and voice bots.
- Knowledge of RAG architectures, AI security, and governance.
- Containerization experience (Docker, Kubernetes).
- MLOps
- Monitoring, logging, and observability for AI systems.

📌 Senior Artificial Intelligence Engineer (Coimbatore)
🏢 Nexa Office Infosystems
📍 Coimbatore