AI Software Engineer

About Sensiwise AI

Sensiwise AI is a UK-based AI consultancy and development company that helps organisations move from AI curiosity to real-world impact. We combine rigorous academic research with practical commercial delivery, offering AI strategy and consulting, hands-on training, custom development, agentic AI, and ethics-first data services.

Founded in 2023, Sensiwise AI is ISO 27001 and ISO 9001 certified company and serves clients across FinTech, healthcare, retail, manufacturing, and legal sectors across Europe and US. With our headquarters in London and a delivery team in India, we are scaling our UK presence through a multi-channel growth strategy spanning startup accelerators, SMEs, and consulting-firm partnerships.

Our proprietary SAIRA™ (Sensiwise AI Readiness Assessment) platform helps businesses benchmark their AI maturity, identify gaps, and build actionable roadmaps. Alongside our consulting work, we run the Sensiwise AI Academy, sharing knowledge across technical and non-technical audiences in multiple languages.

At Sensiwise AI, we believe the best AI is built with rigour, transparency, and a genuine understanding of the problem it is solving. If that is how you like to work, you will fit right in.

Role Purpose

We are looking for an AI Software Engineer who lives at the intersection of applied machine learning and data engineering. Someone who can turn raw, messy client data into reliable pipelines, and turn those pipelines into production AI systems that deliver measurable business value.

You will own the data and model layers of our client projects: designing and building the pipelines that feed AI systems, developing and deploying ML and LLM-powered solutions, and standing up the RAG pipelines and agentic AI systems.

Your role will include planning, tool use, and human-in-the-loop guardrails that power our products. Because much of our work is for regulated industries such as FinTech and healthcare, building AI that is secure by design and safe to deploy. Data privacy and compliance treated as first-class concerns and are central to this role, not an afterthought. Data quality, reliability, and sound engineering matter here too: great AI starts with great data.

You will work directly with senior engineers, the Founder, and clients across FinTech, retail, manufacturing, and SaaS.

Key Responsibilities

Data Engineering & Pipelines

• Design, build, and maintain robust data pipelines (ETL/ELT) that ingest, clean, transform, and serve data for AI and analytics 
• Model and manage data across data warehouses and lakes and operational databases
• Build batch and streaming workflows, orchestrated with tools such as Airflow, Prefect, Dagster, or dbt
• Own data quality, validation, lineage, and observability ensuring the data feeding our models is accurate, timely, and trustworthy
• Engineer features and datasets for model training, evaluation, and retrieval

AI / ML Engineering

• Develop, fine-tune, and evaluate machine learning models using frameworks such as PyTorch, TensorFlow, or scikit-learn
• Deploy models into production as APIs or services, with attention to latency, cost, scalability, and reliability
• Design and implement RAG (Retrieval-Augmented Generation) systems using vector databases (Pinecone, Weaviate, Chroma), including embedding, chunking, and retrieval-quality evaluation
• Integrate LLM APIs (OpenAI, Anthropic, etc.) into production services and product workflows
• Design and build agentic AI systems (multi-step planning, reasoning, tool use, and memory) using frameworks such as LangGraph, LangChain Agents, AutoGen, or CrewAI
• Implement guardrails, human-in-the-loop approvals, and escalation so agents act safely within defined boundaries, and instrument agent evaluation, tracing, and observability (e.g. LangSmith) to improve reliability
• Build AI-powered features such as document processing, intelligent automation, prediction, and data-insight tools

Secure & Compliant AI Development

• Build AI systems secure by design – secure coding, secrets management, least-privilege access, and encryption of data in transit and at rest
• Handle sensitive and regulated client data in line with GDPR, data-protection requirements, and our ISO 27001 controls
• Apply role-based access controls, source restrictions, and audit logging to data pipelines and agent workflows, so systems stay within permitted boundaries
• Contribute to data and model risk reviews, security assessments, and secure deployment practices across client environments

MLOps & Engineering Practices

• Stand up MLOps practices – experiment tracking, model versioning, evaluation, and monitoring (e.g. MLflow, Weights & Biases)
• Monitor deployed models and pipelines for drift, degradation, data issues, and cost, and iterate to improve them
• Containerise and deploy workloads with Docker on cloud platforms (AWS, GCP, and/or Azure), using CI/CD where appropriate
• Write clean, well-documented, testable code, and participate in code reviews and technical design discussions
• Contribute to technical documentation for client deliverables and the internal knowledge base

Client & Project Collaboration

• Work alongside AI consultants and the Founder to deliver client-facing AI and data projects
• Translate business problems and messy real-world data into technical specifications and working systems
• Communicate clearly on data readiness, model performance, progress, blockers, and delivery timelines
• Contribute to demos, prototypes, and proof-of-concept builds for new client engagements, including AI readiness and data assessments

Experience

• 3-5 years of professional experience across AI/ML engineering and/or data engineering in a commercial or consultancy setting
• Strong, demonstrable proficiency in Python for data and ML work
• Advanced SQL and solid experience with relational and non-relational databases
• Proven experience building and maintaining data pipelines (ETL/ELT) in production
• Hands-on experience developing, training, or fine-tuning ML models and deploying them into production
• Practical experience with LLM integration, RAG, or agentic AI systems in real products (not just experiments)
• Familiarity with cloud platforms (AWS, GCP, and/or Azure) and Git-based collaborative workflows
• Awareness of secure development practices and experience handling sensitive or regulated data (e.g. GDPR, ISO 27001, or similar)

Technical Skills

• Python (essential) – pandas, NumPy, and the modern data/ML ecosystem
• SQL – advanced querying, modelling, and optimisation
• Data pipelines & orchestration – Airflow, Prefect, Dagster, or dbt
• Data warehouses / lakes – BigQuery, Snowflake, Redshift, or Databricks
• ML frameworks – PyTorch, TensorFlow, or scikit-learn
• LLM & AI tooling – OpenAI, Anthropic; LangChain or LlamaIndex
• Agentic AI – LangGraph, LangChain Agents, AutoGen, or CrewAI; tool/function calling, orchestration, memory, and guardrails
• RAG & vector databases —-Pinecone, Weaviate, or Chroma
• Databases – PostgreSQL, MongoDB, or Firebase
• Cloud & deployment – AWS, GCP, or Azure; Docker; CI/CD
• Security & compliance – secure coding, secrets management, access controls, data encryption, and GDPR-aware data handling

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent professional experience
• Any certifications in cloud, data engineering, or AI/ML are a plus but not required
• You must have the right to work in the UK. We are not able to offer visa sponsorship for this role at present

Desirable (Nice to Have)

• Experience with streaming and big-data tooling – Apache Kafka, Spark, or Flink
• Experience with MLOps platforms – MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases
• Experience evaluating and optimising RAG systems at scale (retrieval quality, chunking, re-ranking)
• Experience designing multi-agent systems and agent orchestration (LangGraph, AutoGen, CrewAI), including agent evaluation and observability (e.g. LangSmith)
• Familiarity with tool/function calling and the Model Context Protocol (MCP) for connecting agents to external systems and data
• Experience with infrastructure-as-code and serverless data/ML deployment (Lambda, Cloud Run, Azure Functions)
• Exposure to FinTech, retail, or manufacturing data and use cases
• Experience delivering into regulated industries and familiarity with security/compliance frameworks such as ISO 27001, SOC 2, or HIPAA
• Understanding of secure MLOps – model/data governance, threat modelling for AI, and secure handling of model artefacts and secrets
• Understanding of AI ethics, data privacy (GDPR), explainability, and responsible AI principles
• Prior experience in a consultancy or client-facing environment

What We Offer

• A high-impact technical role working directly with the Founder and AI specialists on live client projects
• A competitive salary, offered based on experience
• Deep, structured exposure to the full AI and data stack – pipelines, models, RAG, agents, and MLOps
• A clear progression path toward a Senior AI Software Engineer role as the company scales
• Hybrid, flexible working arrangement based in the UK
• Exposure to UK and international B2B markets and clients
• Access to cutting-edge AI tools, research resources, and the wider Sensiwise AI knowledge base
• A work environment built on academic rigour, ethical AI, and practical delivery