Key Responsibilities:

• Design and Development:Architect, build, and optimize agentic AI systems (e.g., autonomous agents, multi-agent frameworks, LLM-powered agents).
• Integrate agentic AI solutions with existing enterprise platforms and workflows.
• Research and Experimentation:Evaluate emerging agentic AI models, frameworks, and toolkits.
• Prototype and benchmark agentic AI solutions for scalability, robustness, and safety.
• Deployment and Monitoring:Deploying agentic AI agents in production environments, ensuring reliability and performance.
• Monitor agent behaviors, diagnose anomalies, and refine agent policies as needed.
• Developed feedback loops for continuous agent improvement.
• Ethics and Governance:Ensure agentic AI solutions comply with ethical standards, regulatory requirements, and organizational policies.
• Collaboration. Work closely with data scientists, software engineers, product managers, and business stakeholders.
• Communicate complex technical concepts to non-technical audiences.

The Team:

Engineering as a Service provides complete design, implementation, and technology operations, leveraging our core engineering expertise. We transform engineering teams, modernize technology, and deliver complex programs with a product engineering approach. Our flexible delivery models—traditional teams, pools, or pods—are tailored to each client’s needs, offering engineering-led advisory, implementation, and operational capabilities to accelerate innovation.

Qualifications

Must Have Skills/Project Experience/Certifications:

• 6 - 10 years of hands-on experience in Artificial Intelligence, Machine Learning, or related field.
• Knowledge of developing Gen AI or AI/ML solutions from use case to production and an understanding of agentic or autonomous systems.
• Strong understanding of Infrastructure and DevOps related to Gen AI Use case deployments including Cloud environments, LLM Hosting, CICD deployments, docker and Kubernetes clusters.
• Good to Have Skills/Project Experience/Certifications:
• Proficiency in Python development and FastAPI/Flask framework along with SQL
• Familiarity with agentic AI frameworks and concepts - LangChain, LangGraph, AutoGen, Model Context Protocol (MCP), LLMs, Chain of Thought prompting, knowledge store, embeddings
• Autonomous agent development using cloud-based AI services.
• Prompt engineering techniques and fine-tuning.
• Strong understanding of reinforcement learning, planning algorithms, and multi-agent systems.
• Experience with cloud platforms (AWS, Azure, GCP) and deploying AI solutions at scale.