AI Engineer - Data specialist
Actively Hiring
Full-time Posted 19 days ago
Responsibilities
- check_circle Design, build and deploy Generative AI and Agentic AI solutions from prototype to production
- check_circle Develop and optimize RAG pipelines including embeddings, hybrid search, prompt engineering and evaluation frameworks
- check_circle Implement AI agents using frameworks such as LangChain, LangGraph and AutoGen, integrating tools and enterprise workflows
- check_circle Apply modern AI engineering practices, ensuring reproducibility and production readiness in dynamic environments
- check_circle Integrate solutions with enterprise data platforms and cloud services, focusing on scalability and governance standards
- check_circle Leverage tools like Databricks, MLflow and Azure OpenAI for experimentation and deployment
- check_circle Apply DevOps best practices across CI/CD workflows, containerization and automated testing for robust delivery
- check_circle Design and maintain observability and monitoring solutions for AI systems using tools such as Langfuse or Arize
- check_circle Partner with stakeholders to align technical execution with business outcomes and provide technical guidance during architecture discussions
- check_circle Support team knowledge sharing and mentor engineers on AI best practices and delivery standards
Basic qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering or related field; PhD is a plus
- Proven hands-on experience with Generative AI frameworks, LLMs and agentic architectures
- Strong practical knowledge of Databricks ecosystem including Delta Lake, Delta Live Tables and governance features
- Proficiency in Python and working familiarity with SQL or Scala
- Experience implementing RAG architectures and streaming solutions for AI pipelines
- Deployment expertise on Azure or multi-cloud environments and familiarity with containerization tools such as Docker
- Knowledge of AI observability and evaluation solutions for monitoring and performance tuning
- Strong understanding of MLOps, CI/CD practices and infrastructure automation in AI engineering contexts
- Demonstrated ability to lead small teams and communicate effectively across technical and non-technical stakeholder groups
- Experience managing end-to-end delivery from experimentation through production deployment in enterprise contexts
Preferred qualifications
- Familiarity with vector databases such as Pinecone, Weaviate or Milvus
- Knowledge of AI governance protocols including safety guardrails and injection-prevention techniques
- Background working with event-driven architectures or distributed systems
- Experience fine-tuning or training foundational models and applying advanced prompt engineering techniques
- EPAM Employee Stock Purchase Plan (ESPP)
- Protection benefits including life assurance, income protection and critical illness cover
- Private medical insurance and dental care
- Employee Assistance Program
- Competitive group pension plan
- Cyclescheme, Techscheme and season ticket loans
- Various perks such as free Wednesday lunch in-office, on-site massages and regular social events
- Learning and development opportunities including in-house training and coaching, professional certifications, and courses
- If otherwise eligible, participation in the discretionary annual bonus program
- If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
Tags & Focus Areas
Ai Ai Engineer Generative Ai
About EPAM Systems
Shape the future of global data science infrastructure as a Site Reliability Engineer at EPAM. You'll architect, implement, and support cutting-edge platforms like Alteryx, Dataiku, and Azure Machine Learning that power critical business insights across wealth management, investment banking, and corporate functions.
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