Leads the design, development, and productionization of Generative AI and Agentic AI solutions. Builds LLM applications, RAG systems, tool-using agents, data pipelines, APIs, and cloud-native services using Python or Java on AWS. Establishes prompt engineering, evaluation, MLOps, testing, monitoring, security, and reliability practices while collaborating with stakeholders on requirements, metrics, and deployment strategies.
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.
This 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.
Job Responsibilities
- Design, develop, and deploy GenAI and Agentic AI solutions that improve automation, decision-making, and user experience across business workflows.
- Build LLM/SLM-powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents.
- Engineer production-grade services using Java and/or Python (REST/gRPC APIs, microservices, libraries), following secure coding and reliability best practices.
- Develop prompt strategies and prompt engineering assets (templates, routing, guardrails), and implement automated evaluation to improve quality over time.
- Build and maintain data pipelines and processing workflows required for ML/GenAI use cases using cloud services.
- Apply MLOps practices across the lifecycle: experimentation, versioning, CI/CD, deployment, monitoring, and maintenance for models/prompts/agents.
- Implement robust testing (unit/integration), performance benchmarking (latency/cost), and observability (logging/metrics/tracing) for AI services.
- Collaborate with cross-functional stakeholders to define requirements, success metrics, and rollout plans; communicate complex topics clearly to technical and non-technical audiences.
- Strong problem-solving skills and ability to work effectively in ambiguous environments with multiple stakeholders.
Required Qualifications, Capabilities, and Skills
- Undergrad or Master’s degree (or equivalent practical experience) in Computer Science, Data Science, Machine Learning, or related field.
- Hands-on experience building applied AI/ML or GenAI solutions (e.g., RAG, classification, extraction, ranking, summarization, copilots).
- Familiarity with MCP (Model Context Protocol), Agent Skills and architectures that connect models to tools/data through standardized interfaces.
- Familiarity with LLM application patterns: embeddings/vector search, prompt orchestration, tool calling/function calling, safety/guardrails, evaluation.
- Strong software engineering experience delivering production systems; ability to design maintainable architectures and write clean, testable code.
- Proficiency in Java and/or Python and experience building APIs/services and integrating with data sources and downstream systems.
- Experience deploying solutions on AWS and cloud-native environments; understanding of security fundamentals and operational excellence.
- Experience with modern engineering practices: CI/CD, code reviews, unit testing (e.g., pytest/JUnit), and deployment automation.
- Experience with containers and orchestration (e.g., Docker, Kubernetes/EKS) and production monitoring practices.
Preferred Qualifications, Capabilities, and Skills
- Experience building agentic AI systems (multi-step workflows, tool routing, planning, memory patterns, supervision/fallback strategies).
- Experience with AWS Bedrock and/or SageMaker (or equivalent managed ML/GenAI platforms) and deployment patterns for scalable inference.
- Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge, human-in-the-loop review, red teaming).
- Experience fine-tuning models (e.g., LoRA/QLoRA/DoRA) and/or working with SLMs, embeddings, and retrieval systems.
- Experience with developer productivity tooling such as GitHub Copilot and Claude Code, paired with strong SDLC controls.
- Knowledge of the financial services industry and operating in regulated environments (auditability, controls, data handling).
- Exposure to distributed compute/training concepts (e.g., DDP, sharding) and performance/cost optimization.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Similar Jobs
Financial Services
Leads end-to-end delivery of production AI and machine learning solutions for private banking, with emphasis on agentic AI, LLM applications, generative AI guardrails, responsible AI, and model observability. Partners with business stakeholders, establishes engineering standards, supports governance and regulatory requirements, mentors engineers, and delivers secure, scalable services with measurable client and advisor impact.
Top Skills:
Agentic AiCi/CdClaude CodeCloud-Native DeploymentContainerizationDatabricksGenerative AiGithub CopilotKubernetesLarge Language ModelsMachine LearningPythonRetrieval-Augmented Generation
Information Technology • Productivity • Software • Infrastructure as a Service (IaaS)
Own the enterprise pre-sales technical cycle for partners, MSPs, and customers, including discovery, demonstrations, architecture, proof-of-concept support, and technical customer engagements. Collaborate with Account Executives, Product, Training, and Customer Success to drive deals and adoption. Build scalable technical assets, playbooks, lab environments, and sales processes while influencing product roadmaps and representing the company at industry events.
Top Skills:
BashDnsEndpoint ManagementFirewallsHighspotItsm PlatformsJIRALinuxmacOSPowershellRmmSalesforceVbscriptVpnWindows
Artificial Intelligence • Fintech • Greentech • Sales • Software • Travel • Hospitality
Manage end-to-end executive and VIP corporate travel, including air, hotels, ground transport, and bespoke requests. Provide proactive, personalized support across phone, chat, and email while handling urgent disruptions and complex itineraries. Build trusted relationships with senior travelers, balance premium service with productivity and CSAT KPIs, and collaborate with colleagues in a high-performance environment.
Top Skills:
AmadeusGalileoGdsSabre
What you need to know about the Manchester Tech Scene
Home to a £5 billion digital ecosystem, including MediaCity, which consists of major players like the BBC, ITV and Ericsson, Manchester is one of the U.K.'s top digital tech hubs, at the forefront of advancements in film, television and emerging sectors like as e-sports, while also fostering a community of professionals dedicated to pushing creative and technological boundaries.
