AI/ML Engineer (Computer Vision and Agentic AI)

Key Responsibilities Agentic System Development: Design and implement intelligent agent architectures capable of autonomous reasoning, task planning, and execution. Computer Vision Integration: Leverage cutting-edge models (e.g., YOLO, Vision Transformers) to enable AI agents to interpret, analyze, and process visual data effectively. Knowledge Retrieval Optimization: Architect and maintain advanced Retrieval-Augmented Generation (RAG) pipelines, ensuring seamless integration of multi-modal data for context-aware responses. Workflow Orchestration: Utilize industry-standard frameworks, including LangChain and LangGraph, to build modular, resilient, and scalable AI service chains. Cloud Deployment & Scalability: Manage the end-to-end lifecycle of AI solutions, including deployment, monitoring, and performance optimization on cloud infrastructure (AWS, Azure, or GCP). Cross-Functional Collaboration: Collaborate with product and engineering teams to translate abstract business objectives into actionable technical prototypes and production-ready systems. Technical Requirements Experience: 23 years of professional experience in software development with a dedicated focus on AI/ML lifecycle management. Programming: Mastery of Python for robust, production-quality code. AI/ML Foundational Knowledge: Proven expertise in LLMs, RAG, and Computer Vision architectures. Frameworks & Tooling: Orchestration: Hands-on experience with LangChain or LangGraph. Vision Libraries: Proficiency in OpenCV, PyTorch, YOLO, Detectron2, SAM, or ViT. Database Management: Experience with vector databases such as Chroma, Pinecone, or FAISS. Cloud Infrastructure: Demonstrated experience with major cloud platforms (AWS, Azure, or GCP). Problem-Solving: Proven ability to decompose complex technical requirements into scalable, iterative development milestones. Preferred Qualifications Candidates possessing the following attributes will be given priority: Production Lifecycle: Experience transitioning models from development environments (Jupyter/Colab) to live, high-concurrency APIs. Edge AI & Inference: Proficiency in model optimization techniques such as ONNX or TensorRT for low-latency inference. MLOps Proficiency: Experience with containerization (Docker/Kubernetes) and tracking tools (MLflow, Weights & Biases). Advanced Training: Proven experience with Parameter-Efficient Fine-Tuning (PEFT/LoRA) and custom training on specialized datasets. Multi-Modal Expertise: Practical application of modern multi-modal architectures (e.g., Llama Vision, Claude 3.5 Sonnet, GPT-4o). Community Engagement: Evidence of active contributions to open-source AI projects or a strong portfolio of independent AI/ML developments on GitHub. Benefits: Health insurance Leave encashment Provident Fund .