About the Role: AI Engineer
We are seeking a highly skilled AI Engineer with 3 5 years of hands-on experience in AI/ML, Generative AI, and Agentic AI systems. The ideal candidate will have a deep understanding of AI agent frameworks, Model Context Protocol (MCP), Databricks, and cloud-native MLOps workflows. This role involves building, deploying, and optimizing intelligent systems from LLM-powered applications to autonomous multi-agent architectures.
Key Responsibilities
- Design, develop, and deploy machine learning and generative AI models in production environments.
- Build and integrate agentic AI systems intelligent agents capable of reasoning, planning, and multi-step decision-making.
- Develop and maintain data pipelines and MLOps workflows using Databricks, MLflow, and cloud-native tools.
- Integrate LLMs and AI agents with external APIs, databases, and tools using agent frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel, LangGraph).
- Implement and manage Model Context Protocol (MCP) connections between agents and enterprise systems.
- Optimize AI workloads in AWS, Azure, or GCP environments with scalable, secure infrastructure.
- Collaborate with cross-functional teams (data, cloud, and product) to deliver AI-driven solutions.
- Ensure AI system security, observability, explainability, and compliance with governance standards.
Ideal Candidate Profile
AI / ML & Data Science - Strong foundation in machine learning, deep learning, and data science concepts.
- Expertise in Python with ML libraries: PyTorch, TensorFlow, scikit-learn, pandas, NumPy.
- Knowledge of model evaluation, feature engineering, and transfer learning.
- Experience with vector databases (FAISS, Pinecone, ChromaDB).
Generative AI & NLP - Hands-on experience with LLMs, prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning.
- Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks.
- Implementation of text generation, summarization, classification, and document Q&A systems.
Agentic AI, Agent Frameworks & MCP - Deep understanding of agentic AI systems autonomous, multi-step, tool-using architectures.
- Practical experience building AI agents using frameworks such as LangChain, AutoGen, CrewAI, LangGraph, or Semantic Kernel.
- Experience designing multi-agent collaboration systems and task orchestration.
- Hands-on experience implementing or integrating Model Context Protocol (MCP) for tool invocation, context sharing, and agent-to-system communication.
- Strong awareness of safety, governance, auditability, and agent evaluation frameworks.
Databricks, MLOps & Data Engineering - Strong experience with Databricks (Spark, Delta Lake, MLflow, feature store).
- End-to-end experience in data pipelines, ETL/ELT processes, and real-time streaming.
- Proficiency in MLOps best practices: model registry, versioning, drift detection, rollback.
- Observability and automation for deployed ML systems.
Cloud & Infrastructure - Hands-on with AWS / Azure / GCP for AI workloads.
- Familiarity with SageMaker, Azure ML, or Vertex AI.
- Experience with Docker, Kubernetes, and serverless deployments.
- Working knowledge of Infrastructure as Code (Terraform / CloudFormation) and CI/CD pipelines.
Software Engineering & APIs - Strong programming and software design fundamentals.
- Experience building REST / GraphQL APIs and microservices.
- Event-driven and asynchronous architectures (Kafka, Pub/Sub, message queues).
- Integration of AI components with enterprise software systems.
Security, Observability & Responsible AI - Knowledge of monitoring, logging, and tracing (Prometheus, Grafana, OpenTelemetry).
- Implementation of secure AI practices access control, secrets management, prompt injection defense.
- Understanding of model explainability, bias mitigation, and ethical AI considerations.
Preferred / Nice-to-Have
- Familiarity with reinforcement learning and planning-based agents.
- Experience with knowledge graphs and symbolic reasoning.
- Building multi-modal agents (text + vision + audio).
- Contributions to open-source AI / agent frameworks.
- Exposure to edge or on-device AI systems.
- Relevant certifications in Cloud AI / MLOps / Databricks / GenAI are a plus.
