Infosys on cord

Infosys

Senior AI Architect

External position
London, UK
Hiring internally for Infosys?
Consultancy
Transforming Enterprises To Become A Thriving Live Enterprise. AI-Powered. Digital Agility At Scale. Always-On Learning.
Posted
2 months ago
Checked
cord regularly checks that external positions are still open
2 hours ago

Skills & Experience

Job roles: AI Engineer
Experience level: Senior, Lead
Core skills considered: Artificial Intelligence, Machine Learning, Deep Learning, Python, Tensorflow
Other skills considered: PyTorch, Cloud Computing, Kubernetes, Docker, Data Architecture

Logistics

Base salary: Undisclosed
Some companies on cord are unable to disclose salaries publicly due to internal company policies. Message the company for salary information.
Employment type: Permanent
Remote working: Hybrid
Visa sponsorship: Not available

Job Description

We are seeking a highly skilled and experienced Senior Architect/Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval-augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise-scale solutions. As a Senior Architect/Consultant, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models and technologies, and collaborating with cross-functional teams to deliver cutting-edge solutions that meet customer requirements and business objectives.

Primary Skill Set:

  • Generative AI Expertise: In-depth knowledge of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text, code, image, and multimodal generation is essential. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.
  • Agentic AI & Multi-Agent Architecture: Deep expertise designing autonomous and multi-agent systems that reason, plan, and act using tools. Command of agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google's Agent Development Kit (ADK). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi-step task execution at scale.
  • Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern MCP servers and clients and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing scalable, enterprise-grade agentic ecosystems.
  • Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills-packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams.
  • Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems-chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.
  • LLMOps, Evaluation & Responsible AI: Experience operationalizing LLM and agentic systems at scale-evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails and red-teaming; and continuous optimization of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.
  • Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.
  • Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.
  • Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP-based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost- and latency-efficient system design for enterprise-grade AI.

Secondary Skill Set:

  • Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.
  • Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.
  • AI Governance, Security & Responsible AI: Familiarity with AI governance, safety, and compliance considerations-data privacy, security, bias and fairness, transparency, auditability, and emerging AI regulations-and how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.
  • Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non-technical stakeholders.

Roles & Responsibilities:

  • Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmap-identifying opportunities, evaluating potential use cases, and proposing innovative, agent-driven solutions that align with business goals.
  • Model Selection: Evaluate and select appropriate models, agent frameworks, RAG strategies, and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability, complexity, safety, cost, latency, and computational resources.
  • Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.
  • Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AI-agent orchestration, MCP-based tool/data integration, shared skills and connectors, memory and state management, guardrails, human oversight, and observability-to enable safe, reliable, and scalable production deployment across teams.
  • Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.
  • Performance Optimization: Continuously optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.
  • Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.
  • Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.
  • Team Collaboration: Collaborate effectively with cross-functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.
Senior AI Architect at Infosys
Position posted 2 months ago

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Starling Bank on cord

Starling Bank

Full Stack Engineer (ML Ops)

London, UK
FinTech · Banking · Finance
A better bank for everyone
Active
over 6 months ago
Responds to
0% of requests
Responds in
1h 55m
Requests
7 pending

Skills & Experience

Job roles: Full Stack
Experience level: Mid, Senior
Core skills considered: Java, React, Redux, AWS, JavaScript

Logistics

Base salary: £70K - £110K
Employment type: Permanent
Remote working: Hybrid (up to 2 remote days p/w)
Visa sponsorship: Not available

Job Description

Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.

Starling is the UK’s first and leading digital bank on a mission to fix banking! We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way.

We’re a fully licensed UK bank with the culture and spirit of a fast-moving, disruptive tech company. We’re a bank, but better: fairer, easier to use and designed to demystify money for everyone. We employ more than 3,000 people across our London, Southampton, Cardiff and Manchester offices.

Our technologists are at the very heart of Starling and enjoy working in a fast-paced environment that is all about building things, creating new stuff, and disruptive technology that keeps us on the cutting edge of fintech. We operate a flat structure to empower you to make decisions regardless of what your primary responsibilities may be, innovation and collaboration will be at the core of everything you do. Help is never far away in our open culture, you will find support in your team and from across the business, we are in this together!

The way to thrive and shine within Starling is to be a self-driven individual and be able to take full ownership of everything around you: From building things, designing, discovering, to sharing knowledge with your colleagues and making sure all processes are efficient and productive to deliver the best possible results for our customers. Our purpose is underpinned by five Starling values: Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.

Hybrid Working

We have a Hybrid approach to working here at Starling - our preference is that you're located within a commutable distance of one of our offices so that we're able to interact and collaborate in person. We don't like to mandate how much you visit the office and work from home, that's to be agreed upon between you and your manager.

Our Data Environment

Our Data teams are excited about the value of data within the business, powers our product decisions to improve things for our customers and enhance effective and agile decision making, regardless of what their primary tech stack may be. Hear from the team in our latest blogs or our case studies with Women in Tech.

We are looking for talented data professionals at all levels to join the team. We value people being engaged and caring about customers, caring about the code they write and the contribution they make to Starling. People with a broad ability to apply themselves to a multitude of problems and challenges, who can work across teams do great things here at Starling, to continue changing banking for good.

Requirements

We have built our entire banking platform in house and mostly in Java. We are looking for people who want to work on building the tooling that is used by our engineers on a daily basis.

We are looking for people who are truly full stack, and are as comfortable polishing their javascript front end as they are debugging the innards of their java applications database interactions, or standing up infrastructure with terraform. We are looking for people who can:

  • Design REST apis.
  • Code backend services, ideally using Java, or another other server side compiled language.
  • Develop modern front ends, ideally using React and Redux.
  • Get their code into the cloud and support it there, ideally on AWS.
  • Believe in clean coding, simple solutions, automated testing and continuous deployment.
  • Like to take ownership of a feature from the original idea through to live.
  • Think (like us) that a small number of empowered developers is the right way to deliver software.

Company Benefits

  • 33 days holiday (including flexible bank holidays)
  • An extra day’s holiday for your birthday
  • 16 hours paid volunteering time a year
  • Part-time and/or flexible hours available for most roles
  • Salary sacrifice, company enhanced pension scheme
  • Life insurance at 4x your salary
  • Hybrid/remote working
  • Private Medical Insurance with VitalityHealth including mental health support and cancer care. Partner benefits include discounts with Waitrose, Mr&Mrs Smith and Peloton
  • Generous family-friendly policies
  • Varied social groups set up and run by our employees
  • Perkbox membership giving access to retail discounts, a wellness platform for physical and mental health, and weekly free and boosted perks
  • Access to initiatives like Cycle to Work, Salary Sacrificed Gym partnerships and Electric Vehicle (EV) leasing

Full details are available on our careers site

Interview Process

Interviewing is a two way process and we want you to have the time and opportunity to get to know us, as much as we are getting to know you! Our interviews are conversational and we want to get the best from you, so come with questions and be curious. In general you can expect the below, following a chat with one of our Talent Team:

  • Stage 1 - 30 mins with one of the team
  • Stage 2 - Take home challenge
  • Stage 3 - 90 mins technical interview with two team members
  • Stage 3 - 45 min final with an executive and a member of the people team
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