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Forward Deployment Engineer
Forward Deployment Engineer (AI/ML Focus)
Location: Glasgow / Knutsford – Remote or office-based
About the Role
Hackajob is collaborating with Barclays to identify exceptional professionals for this Forward Deployment Engineer position—where technologists drive AI and software innovation to deliver modern financial solutions.
As a Forward Deployment Engineer, your mission is to design, develop, and improve AI-powered software that enables business agility, platform scalability, and cutting-edge capabilities for Barclays’ customers and teams. This role blends AI/ML expertise, software engineering, and problem-solving to improve decision-making processes across banking.
Responsibilities
Core Accountabilities (Technical Focus)
- Develop and ** prototype to production ** high-quality software solutions, leveraging:
- AI/ML paradigms (LLMs, RAG, NLP) and modern frameworks (LangChain, LlamaIndex, Hugging Face, Strands, FastMCP).
- Cloud platforms (AWS/Azure) for scalable deployment.
- Full-stack architectures (Python + APIs) to deliver robust production systems.
- Collaborate cross-functionally with:
- Product managers to define requirements.
- Designer/engineer teams to harmonise technical blueprints with business goals.
- Data/ML stakeholders to refine AI-driven workflows.
- Secure, optimise, and test code using MLOps practices for monitoring, CI/CD pipelines, and long-term model maintenance.
Strategic Leadership & Influence
- Lead or guide collaborative initiatives—identify technical gaps, advocate for best practices, and drive direction for complex projects or assignments.
- Build trust with business teams to consult on AI/ML adoption (risk, compliance, AI responsibility in financial services).
- Drive capability-building within teams and peers through mentoring, knowledge-sharing, and creative problem-solving.
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Requirements
Technical Competencies
✅ Experience in AI/ML platforms:
- Hands-on experience with generative AI, LLMs, RAG architectures, and specialty frameworks (Strands, LangChain).
- Knowledge of model deployment pipelines (FastMCP, A2A). ✅ Software Engineering:
- Proficient in Python for AI/ML applications.
- Cloud expertise in AWS or Azure for scalable infrastructure.
- Full-stack problem-solving, including REST/gRPC APIs, frontend (React/JS) integration. ✅ MLOps & Productionisation:
- Model deployment, tracking, validation, and ethics-enforced AI solutions.
- CI/CD pipelines and optimisation techniques.
Domain Expertise
- Experience in financial domains: investment banking, risk management, compliance, or regulatory-conscious AI.
- Fit within Assistant Vice President expectations (briefed below) if the future involves team leadership or broader governance.
Soft Skills
🔹 Stakeholder Communication: Translate engineering complexity into clear, actionable insights for business teams. 🔹 Consulting Acumen: Balance urgency with long-term roadmaps in agile environments. 🔹 Risk Sensitivity: Mitigate ethical and operational complexities (AI Fairness, Secure Coding Practices). 🔹 Solution Habitat: Ability to elevate technical challenges into full operational solutions.
Assistant Vice President Expectations
(If transforming to leadership/strategic level)
Now or in the future, as this role accounts for high-impact ownership, look for ** zelo in direction-setting skills**:
1️⃣ Leadership Style: Embrace the four LEAD behaviours (L = Authentic Listening, E = Inspiring Energy, A = Alignment, D = Developing Others) to empower teams. 2️⃣ Decisive Governance:
- Advise on policy/technology governance for risk, compliance, and control.
- Influence senior stakeholders to drive strategic alignment. 3️⃣ Performance Management:
- Appraise subordinates, evolve benchmarks, and deliver rewards tied to objectives. 4️⃣ Multi-Source Challenges: Resolve cross-functional conflicts, adopt creative solutions, and mine intuition from diverse stakeholders for risk mitigation.
Get help with your application
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Reminder: Barclays expects values leadership—all colleagues must embody Integrity, Respect, Service, Excellence, and Stewardship, outscaling assumptions into actions aligned with the Barclays Mindset (Empower • Challenge • Drive).
Some Highly Valued (But Not Required) Skills
Combine this with core qualifications for competitive standing:
- Prompt Engineering & custom fine-tuning strategies.
- Vector databases (e.g., Pinecone, Weaviate) for knowledge retrieval.
- Data pipelines/analytics (Lakehouse, BigQuery + visualisation tools).
- React/JS expertise for ML-driven UIs.
Ask focusing directly on critical skills and assessments: risk controls, digital growth, strategic think, and technical processes. Assess envisaged panic:
- Risk/Compliance*: How would you align AI/ML outputs with KYC or AML regulations?
- Business Acumen: A non-LED firm’s UX team is upset that their chatbot’s response hurts conversions—how would you diagnose and redeploy?
- Technology Outlook: How do you prioritise between a Falcon model update vs. refactoring old compliance code?
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