Artificial Intelligence / Machine Learning Lead

Role Type : Full-time | Hands-On AI/ML Leadership
Role Overview :
We are seeking a hands-on AI/ML Lead who will design, develop, and deploy AI-driven lending automation solutions with a focus on NLP workflows, GenAI orchestration, and valuation process automation. This is a 50% coding and delivery role, 50% architecture and leadership role. You will work directly with our Engineering Lead, Data Engineering Team, Product Managers, and UK-based stakeholders to define, architect, and integrate AI capabilities seamlessly into our Node.js + React enterprise platform.
Key Responsibilities :
AI Solution Architecture & Development :
- Architect and implement end-to-end AI/ML features aligned with lending workflows :
1. NLP Document Parsing OCR, entity extraction, semantic search, summarization, classification.
2. Valuation Automation Predictive modeling for asset and loan valuations.
3. GenAI Orchestration Multi-step, multi-service automation workflows.
- Develop production-grade AI services in Python, integrating with REST/GraphQL APIs, microservices, and event-driven architectures.
- Integrate GenAI APIs (OpenAI GPT, Anthropic Claude, AWS Comprehend, Google Vertex AI, etc.) into existing systems.
Model Lifecycle Management :

- Select, train, fine-tune, and deploy models (LLMs, transformer-based models, classical ML).
- Implement model monitoring pipelines for accuracy, drift detection, bias evaluation, and retraining triggers.
- Optimize inference latency, throughput, and scalability for production workloads.
Data Readiness & Governance :
- Collaborate with Data Engineering to ensure AI-ready data pipelines (schema design, storage format, vectorization strategies).
- Establish data labeling, augmentation, and versioning processes for supervised and semi-supervised learning.
- Ensure compliance with data privacy regulations (GDPR, RBI guidelines) and ethical AI principles.
AI Workflow Orchestration :
- Design and implement multi-step AI orchestration layers combining LLM prompts, RAG (Retrieval-Augmented Generation), and business rules.
- Build custom prompt chains and tools to handle complex workflows like Credit Committee Pack creation and communication parsing.
Stakeholder Collaboration & Leadership :
- Translate complex AI concepts into clear business benefits for non-technical stakeholders.
- Mentor and guide developers on AI integration best practices.
- Track AI feature KPIs and demonstrate measurable business impact.
Required Skills & Experience :
- Total Experience : 7 - 12 years in software engineering, with 3+ years hands-on AI/ML solution delivery.
- Proven record of deploying AI/NLP features in production environments.
- Proficiency in Python (FastAPI, Flask, LangChain, Hugging Face Transformers, PyTorch/TensorFlow).
- Strong experience with NLP pipelines tokenization, embeddings, semantic search, summarization, classification, sentiment analysis.
- Expertise in AI orchestration frameworks (LangChain, Haystack, LlamaIndex) and workflow automation.
- Proficient in REST API design, microservices, and integrating AI with JavaScript/TypeScript-based backends.
- Deep understanding of data structures, feature engineering, and vector databases (Pinecone, Weaviate, FAISS).
- Solid grasp of MLOps tools (MLflow, Kubeflow, AWS SageMaker, Azure ML).
- Familiarity with cloud-native AI deployments (AWS, Azure, GCP).
- Strong communication skills for technical-to-business translation.
Bonus Skills :
- Fintech or lending automation platform experience.
- Familiarity with financial document workflows (KYC, underwriting, valuation reports).
- Hands-on experience with RAG pipelines, prompt engineering, and custom LLM training.