Senior Artificial Intelligence Engineer

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.
• Strong 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.