{"id":178759,"url":"https://alion.io/job/jpmorganchase-applied-ai-ml-lead-python-agentic-ai","title":"Applied AI ML Lead - Python & Agentic AI","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":90,"open_postings":1160,"ghost_share":0.011,"stale_share":0.553,"repost_share":0.058,"time_to_fill_p50_days":12,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Glasgow, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":131000,"max_usd":254000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Java","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Amazon EKS","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Docker","optional":true},{"name":"Embeddings","optional":true},{"name":"Fine-tuning","optional":true},{"name":"gRPC","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"JUnit","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"LoRA","optional":true},{"name":"Machine Learning","optional":true},{"name":"PEFT","optional":true},{"name":"Pytest","optional":true},{"name":"QLoRA","optional":true},{"name":"Red Teaming","optional":true},{"name":"Rest API","optional":true}],"status":"live","first_seen_at":"2026-08-31T00:00:00Z","employer_posted_date":"2026-08-31","last_verified_at":"2026-11-01T00:28:33Z","board_verified":true,"closed_at":null,"days_open":62,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":61},"description":"As a Applied AI/ML Lead Engineer in our Applied AI ML - Python & Agentic AI team, you will design, build, and productionize Generative AI and Agentic AI solutions. The ideal candidate brings a balanced mix of modern AI/ML delivery (LLMs/SLMs, RAG, tool-using agents, evaluation, MLOps) and backend/service engineering (Java and/or Python, APIs/microservices, testing, CI/CD, observability, reliability) on AWS and cloud-native platforms.\nThis role values modern AI engineering workflows and tooling such as GitHub Copilot and Claude Code to accelerate delivery while maintaining quality and security. Familiarity with MCP (Model Context Protocol), Agent Skills and designing agentic systems that integrate models with tools and enterprise data via structured interfaces is a plus.\nJob Responsibilities\nDesign, develop, and deploy GenAI and Agentic AI solutions that improve automation, decision-making, and user experience across business workflows.\nBuild LLM/SLM-powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents.\nEngineer production-grade services using Java and/or Python (REST/gRPC APIs, microservices, libraries), following secure coding and reliability best practices.\nDevelop prompt strategies and prompt engineering assets (templates, routing, guardrails), and implement automated evaluation to improve quality over time.\nBuild and maintain data pipelines and processing workflows required for ML/GenAI use cases using cloud services.\nApply MLOps practices across the lifecycle: experimentation, versioning, CI/CD, deployment, monitoring, and maintenance for models/prompts/agents.\nImplement robust testing (unit/integration), performance benchmarking (latency/cost), and observability (logging/metrics/tracing) for AI services.\nCollaborate with cross-functional stakeholders to define requirements, success metrics, and rollout plans; communicate complex topics clearly to technical and non-technical audiences.\nStrong problem-solving skills and ability to work effectively in ambiguous environments with multiple stakeholders.\nRequired Qualifications, Capabilities, and Skills\nUndergrad or Master’s degree (or equivalent practical experience) in Computer Science, Data Science, Machine Learning, or related field.\nHands-on experience building applied AI/ML or GenAI solutions (e.g., RAG, classification, extraction, ranking, summarization, copilots).\nFamiliarity with MCP (Model Context Protocol), Agent Skills and architectures that connect models to tools/data through standardized interfaces.\nFamiliarity with LLM application patterns: embeddings/vector search, prompt orchestration, tool calling/function calling, safety/guardrails, evaluation.\nStrong software engineering experience delivering production systems; ability to design maintainable architectures and write clean, testable code.\nProficiency in Java and/or Python and experience building APIs/services and integrating with data sources and downstream systems.\nExperience deploying solutions on AWS and cloud-native environments; understanding of security fundamentals and operational excellence.\nExperience with modern engineering practices: CI/CD, code reviews, unit testing (e.g., pytest/JUnit), and deployment automation.\nExperience with containers and orchestration (e.g., Docker, Kubernetes/EKS) and production monitoring practices.\nPreferred Qualifications, Capabilities, and Skills\nExperience building agentic AI systems (multi-step workflows, tool routing, planning, memory patterns, 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