Senior AI/ML Engineer

we're looking for a Gen AI Engineer to join our dynamic team and play a pivotal role in building AI solutions that will define the future of intelligent systems. If you're excited about applying your expertise to projects that disrupt industries and drive measurable impact, we want to hear from you! This is an exciting opportunity for self-motivated engineers with technical expertise, creative problem-solving skills, and a talent for disruptive process transformation using Gen AI What you'll do: Lead with autonomy: Take ownership of Gen AI projects from ideation to deployment, pushing boundaries of innovation Design the future: Develop and fine-tune Generative AI models (LLMs, diffusion models, GANs, VAEs, etc) to optimize SCP business processes and enhance productivity; Empower AI agents: Create agent AI architectures for autonomous decision-making, task delegation, and multi-agent collaboration using Agentic AI frameworks like AutoGPT. Innovate with LLM s: Build optimize LLM s applications, leveraging RAG and build robust Machine Learning pipelines for NLP, Multimodal AI tasks; Work with cutting-edge tools: Harness the power of Vector Databases (eg, Pinecone, FAISS, ChromaDB) and LLM APIs (OpenAI, Anthropic, Hugging Face, Mistral, Llama). Collaborate for impact: Partner with cross-functional teams to integrate AI solutions into real-world applications like chatbots, copilots, automation tools, etc). Stay ahead: Perform continuous research on state-of-the-art AI methodologies, exploring advancements in Generative AI, Autonomous Agents, andNLP to drive innovation. Required Skills Experience [Must Have] 9 13 years of experience in AI/ML and software engineering Proven experience building multi-agent AI systems in production environments Hands-on experience designing and operating MCP servers for agent orchestration and lifecycle management Strong experience with LLMs, RAG, and agent frameworks (LangGraph, AutoGen, CrewAI, custom frameworks, etc) Solid background in deep learning (PyTorch, TensorFlow) and traditional ML techniques Strong understanding of agent communication patterns, tool use, memory, and planning Expertise in Python and backend service development (FastAPI, REST/gRPC) Experience deploying AI systems in cloud environments (AWS, Azure, or GCP) Strong experience with observability, monitoring, and reliability engineering for AI services Hands-on knowledge of MLOps / LLMOps best practices Good to Have Experience with event-driven or distributed agent systems Knowledge of security, access control, and compliance in agent platforms Experience optimizing latency and cost in multi-agent and LLM-heavy systems