Matellio Inc.
Website:
matellio.com
Job details:
We are looking for an experienced AI/ML Lead with deep expertise in Traditional Machine Learning, Deep Learning, Generative AI (LLMs), Agentic AI, Model Context Protocol (MCP), MLOps, and AI Evaluation Harness Engineering. The ideal candidate will lead the architecture, design, development, and deployment of enterprise-scale AI solutions while driving technical strategy, mentoring engineering teams, and collaborating with business and product stakeholders to deliver scalable, production-ready AI platforms.
Experience: 7+ Years
Location: Jaipur/Jodhpur/Remote
Key Responsibilties
- Lead the architecture, design, and implementation of enterprise-grade AI/ML and Generative AI solutions from concept to production.
- Define end-to-end AI solution architecture, including data pipelines, model orchestration, infrastructure, security, scalability, and deployment strategies.
- Architect and build scalable RAG applications, LLM-powered platforms, Agentic AI systems, and MCP-enabled AI solutions integrated with enterprise applications, APIs, and external tools.
- Design robust multi-agent architectures and intelligent workflows leveraging Model Context Protocol (MCP) for standardized communication across AI agents and enterprise systems.
- Own the complete AI/ML lifecycle, including model development, validation, deployment, monitoring, drift detection, optimization, and continuous improvement.
- Lead the implementation of MLOps practices, AI evaluation frameworks, Harness Engineering, automated benchmarking, observability, and governance.
- Establish AI architecture standards, reusable frameworks, best practices, and design patterns across engineering teams.
- Collaborate with Product, Engineering, Data, and Business teams to translate business challenges into scalable AI architectures and technical solutions.
- Provide technical leadership through architecture reviews, design discussions, code reviews, and mentoring of AI/ML engineers.
- Drive technical decision-making for model selection, infrastructure, cloud architecture, cost optimization, performance tuning, and AI platform scalability.
- Support client engagements and pre-sales initiatives by creating solution architectures, technical proposals, effort estimations, and participating in technical discussions.
Required Skills
- Strong proficiency in Python, Statistics, Algorithms, and Data Structures.
- Extensive experience in Traditional Machine Learning, including feature engineering, model selection, hyperparameter tuning, and evaluation metrics (Precision, Recall, F1 Score, ROC-AUC).
- Strong understanding of Deep Learning architectures, including CNNs, RNNs, Transformers, and modern foundation models.
- Hands-on experience with PyTorch, TensorFlow, and Scikit-learn.
- Proven expertise in designing, deploying, and optimizing Large Language Model (LLM) applications using both commercial and open-source models.
- Strong experience building scalable Retrieval-Augmented Generation (RAG) applications.
- Deep understanding of embeddings, semantic search, vector indexing, chunking strategies, prompt engineering, and context optimization.
- Experience implementing LLM evaluation, hallucination mitigation, AI observability, safety guardrails, and responsible AI practices.
- Hands-on experience architecting and deploying production-grade Agentic AI applications and autonomous AI workflows.
- Strong expertise in implementing Model Context Protocol (MCP) for integrating AI agents with enterprise systems, APIs, tools, and multi-agent environments.
- Experience with fine-tuning techniques including LoRA and QLoRA.
- Expertise in developing AI evaluation harnesses, automated testing, benchmarking frameworks, and performance validation pipelines.
- Strong understanding of:
- Experiment tracking and reproducibility
- Model and prompt versioning
- Automated benchmarking and comparative evaluation
- CI/CD pipelines and end-to-end MLOps practices
- Experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar.
- Hands-on experience with vector databases such as Pinecone, OpenSearch, FAISS, ChromaDB, or Weaviate.
- Experience with AWS AI Services, including Amazon SageMaker, Amazon Bedrock (Agents, Guardrails, Knowledge Bases) and related AWS cloud services.
- Strong knowledge of Docker, Kubernetes, REST APIs, Git, CI/CD, cloud-native architecture, and distributed systems.
- Excellent leadership, stakeholder management, communication, and solution architecture skills with the ability to mentor teams and drive enterprise AI initiatives.
Click on Apply to know more.