Senior Agentic AI Engineer Python

• Minimum 3 years of experience building and deploying production-grade applications using the Python ecosystem
• Strong Python proficiency for automation and data processing, with hands-on experience packaging and integrating ML services into real applications
• Advanced experience with GenAI tools, LLMs (Claude, GPT, and others), and code generation tools such as GitHub Copilot or Windsurf
• Working knowledge of agentic AI frameworks such as LangGraph or CrewAI, MCP for tool and context integration, and familiarity with decision-making and planning frameworks
• Proven experience with cloud-based AI/ML services on AWS, Azure, or GCP, and solid understanding of the full AI/ML project lifecycle
• Familiarity with CI/CD/CT practices for ML systems and workflow orchestration tools such as Kubeflow or Airflow
• Master's or Ph.D. degree in Computer Science or a related field
• Strong interpersonal, analytical, and problem-solving skills
• Upper-intermediate or higher proficiency in English, both spoken and written

In this role, you will design and build next-generation agentic AI systems — including LLM-powered code generation pipelines, multi-agent workflows, and RAG applications — within SoftServe's AI and Data Science Center of Excellence, a team of 170+ experts including Data Scientists, ML Engineers, and Architects. Your work will directly shape how clients leverage autonomous AI at production scale, from requirements gathering through to deployment.

SoftServe is an equal opportunity employer. Qualified applicants will receive consideration regardless of race, color, ancestry, ethnicity, national origin, religion, sex, sexual orientation, gender identity or expression, age, citizenship, disability, health condition, marital or family status, veteran status, or any other characteristic protected by applicable law.

,[Design and develop agentic pipelines for code generation, using LLMs and agent frameworks to produce functional, high-quality code artifacts aligned with business and technical requirements, Build and maintain LLMOps pipelines using tools such as MLflow, Langfuse, or LangSmith to ensure model observability, reproducibility, and prompt versioning across production workflows, Collaborate closely with clients and stakeholders to understand use cases, gather requirements, and translate them into scalable, production-ready agentic ML solutions, Design and implement MCP (Model Context Protocol) integrations to connect LLMs with external tools, APIs, and data sources, extending the capabilities of agentic systems in a standardised, interoperable way, Develop and deploy applications for NLP, RAG systems, and multimodal models (text and image), integrating them into end-to-end ML service architectures, Act as human-in-the-loop to validate agentic outputs against quality, compliance, and contextual standards, Support the productionization of large language models and advanced AI systems, including CI/CD/CT pipeline setup and cloud deployment on AWS, Azure, or GCP, Contribute to the team's knowledge base by capturing reusable prompt patterns, templates, and playbooks that accelerate discovery and delivery] Requirements: Python, GitHub, AI, AWS, Azure, GCP, Kubeflow, Degree