Posted:5 days ago|
Platform:
On-site
Full Time
We are seeking a Generative AI Engineer to join our advanced AI engineering team. This role demands strong foundations in Python and hands-on expertise in Crew AI framework for building agentic AI solutions. The ideal candidate is experienced in memory management, guardrails implementation, caching techniques, and follows industry best practices to ensure scalable, safe, and efficient AI systems.
The engineer will be responsible for designing, developing, and deploying agentic AI solutions, embedding safety and reliability into the lifecycle, and working closely with architects, product managers, and business stakeholders.
Generative AI Solution Development - Build and maintain AI agents and workflows using Crew AI framework. Implement advanced memory management strategies. Integrate caching techniques to optimize performance.
Responsible AI and Guardrails - Design and enforce guardrails to ensure safe and policy-compliant AI behavior. Mitigate risks such as hallucinations and unsafe outputs.
Core Engineering - Develop clean, modular, and scalable Python code for agent orchestration. Design APIs and integrate AI components with enterprise applications.
Collaboration and Delivery - Work with architects and domain experts to design AI-driven applications. Collaborate with product managers and UX designers. Document solutions and best practices.
Innovation and Best Practices - Research and evaluate new techniques in generative AI. Contribute to evolving design patterns for agentic AI applications.
Strong programming expertise in Python.
Proven experience in Crew AI framework for multi-agent systems.
Understanding of memory management techniques.
Practical knowledge of guardrails frameworks.
Familiarity with caching strategies in AI workflows.
Experience with LLM evaluation, prompt engineering, and fine-tuning.
Knowledge of cloud platforms (AWS, Azure, GCP).
Graduation (Bachelor's or Master's) in Artificial Intelligence, Machine Learning, or Computer Science.
Experience with LangChain or LlamaIndex.
Hands-on with observability tools like OpenTelemetry or LangSmith.
Exposure to MLOps, CI/CD, and containerization (Docker, Kubernetes).
Understanding of regulatory and compliance considerations in AI adoption.
Certifications in AI/ML or cloud AI services are a plus.
Genpact
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