Job Description
Iteration and is comfortable treating AI capabilities as real world product features (not standalone experiments).
Responsibilities
AI & LLM Engineering
- Deploy and fine-tune LLMs on on-premise / private infrastructure (no external API dependency)
- Build backend architecture by developing NLP pipelines and implement RAG (Retrieval Augmented Generation) pipelines using local vector databases
- Optimize models for performance, memory usage, and inference latency on internal hardware
- Design AI components with clear product use cases and user workflows in mind
- Translate functional requirements into AI capabilities that can be embedded into applications
Document Intelligence
- Design and develop secure backend services (Python-based preferred) to orchestrate: Document ingestion, LLM inference, Scoring and comparison logic
- Build robust pipelines to process multi-format documents (DOCX, PDF, scanned documents, etc.)
- Handle document chunking, embeddings, metadata tagging, and version comparison
- Design explainable outputs for document reviewers (traceability to source clauses)
- Integrate with Microsoft ecosystem: Share Point document repositories and MS Office file formats
- Ensure the entire system functions fully offline within a restricted network
· Apply cloud computing fundamentals (compute, storage, networking) effectively
Security & Compliance
- Follow enterprise-grade security practices: No external data transfer, secure credential and access handling, Role-based access control (RBAC) support
- Align implementation with client IT/security requirements
- Design systems with logging, monitoring, audits and traceability in mind
- Familiarity with confidential computing and private networking patterns is a plus (e.g., Bastion access, Private Link/Private Endpoints, private DNS, Key Vault/secret management).
- Ensure compliance with ethical AI practices and regulatory frameworks.
Collaboration & Ownership
- Collaborate with frontend engineers to support UI requirements
- Participate in solution design discussions with client stakeholders
- Own components end-to-end—from POC to production deployment
- Contribute to technical documentation of assumptions, risks, decisions and deployment runbooks
- Act as a technical interface with client IT and legal stakeholders to clarify requirements and acceptance criteria.
- Own end-to-end delivery of assigned AI features/components (scope, milestones, acceptance criteria).
- Support demos, walkthroughs, UAT readiness, and handover with documentation/runbooks.
Qualifications
Core Technical Skills
- 3–5 years of hands-on experience in AI / ML engineering and Strong proficiency in Python
- Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals.
- Solid understanding of NLP fundamentals, transformer architectures, embeddings and semantic search
- Strong experience working with structured and unstructured data, including preprocessing, validation, and transformation for AI pipelines
- Hands-on experience with open-source LLMs (e.g., LLa MA, Mistral, Falcon, etc.)
- Experience deploying models locally or on private servers (not just cloud APIs)
- Experience with frameworks such as Hugging Face, Lang Chain / Llama Index (or similar orchestration frameworks)
- Vector databases (FAISS, Chroma, Milvus, etc.)
- Experience with prompt engineering and structured outputs
- Ability to plan work, estimate, and deliver independently in a client-facing or delivery driven environment.
Systems & Infrastructure
- Experience building and deploying backend services (Fast API, Flask, or similar) in banking, finance or other regulated
- Familiarity with Linux environments and GPU-based inference
- Experience with working on containerization and clustering (Docker preferred)
- Experience working in restricted / air-gapped environments is a big plus
- Familiarity with logging, monitoring, and troubleshooting of deployed services
· Experience with information security and secure development best practices.
Education
- Bachelor’s or Master’s degree in: Computer Science, Data Science or Artificial Intelligence
- Exposure to banking, finance, or regulated enterprise environments
- Experience optimizing models for low-latency inference
- Familiarity with UI-driven AI workflows (human-in-the-loop systems)
Responsibilities
AI & LLM Engineering
- Deploy and fine-tune LLMs on on-premise / private infrastructure (no external API dependency)
- Build backend architecture by developing NLP pipelines and implement RAG (Retrieval Augmented Generation) pipelines using local vector databases
- Optimize models for performance, memory usage, and inference latency on internal hardware
- Design AI components with clear product use cases and user workflows in mind
- Translate functional requirements into AI capabilities that can be embedded into applications
Document Intelligence
- Design and develop secure backend services (Python-based preferred) to orchestrate: Document ingestion, LLM inference, Scoring and comparison logic
- Build robust pipelines to process multi-format documents (DOCX, PDF, scanned documents, etc.)
- Handle document chunking, embeddings, metadata tagging, and version comparison
- Design explainable outputs for document reviewers (traceability to source clauses)
- Integrate with Microsoft ecosystem: Share Point document repositories and MS Office file formats
- Ensure the entire system functions fully offline within a restricted network
· Apply cloud computing fundamentals (compute, storage, networking) effectively
Security & Compliance
- Follow enterprise-grade security practices: No external data transfer, secure credential and access handling, Role-based access control (RBAC) support
- Align implementation with client IT/security requirements
- Design systems with logging, monitoring, audits and traceability in mind
- Familiarity with confidential computing and private networking patterns is a plus (e.g., Bastion access, Private Link/Private Endpoints, private DNS, Key Vault/secret management).
- Ensure compliance with ethical AI practices and regulatory frameworks.
Collaboration & Ownership
- Collaborate with frontend engineers to support UI requirements
- Participate in solution design discussions with client stakeholders
- Own components end-to-end—from POC to production deployment
- Contribute to technical documentation of assumptions, risks, decisions and deployment runbooks
- Act as a technical interface with client IT and legal stakeholders to clarify requirements and acceptance criteria.
- Own end-to-end delivery of assigned AI features/components (scope, milestones, acceptance criteria).
- Support demos, walkthroughs, UAT readiness, and handover with documentation/runbooks.
Qualifications
Core Technical Skills
- 3–5 years of hands-on experience in AI / ML engineering and Strong proficiency in Python
- Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals.
- Solid understanding of NLP fundamentals, transformer architectures, embeddings and semantic search
- Strong experience working with structured and unstructured data, including preprocessing, validation, and transformation for AI pipelines
- Hands-on experience with open-source LLMs (e.g., LLa MA, Mistral, Falcon, etc.)
- Experience deploying models locally or on private servers (not just cloud APIs)
- Experience with frameworks such as Hugging Face, Lang Chain / Llama Index (or similar orchestration frameworks)
- Vector databases (FAISS, Chroma, Milvus, etc.)
- Experience with prompt engineering and structured outputs
- Ability to plan work, estimate, and deliver independently in a client-facing or delivery driven environment.
Systems & Infrastructure
- Experience building and deploying backend services (Fast API, Flask, or similar) in banking, finance or other regulated
- Familiarity with Linux environments and GPU-based inference
- Experience with working on containerization and clustering (Docker preferred)
- Experience working in restricted / air-gapped environments is a big plus
- Familiarity with logging, monitoring, and troubleshooting of deployed services
· Experience with information security and secure development best practices.
Education
- Bachelor’s or Master’s degree in: Computer Science, Data Science or Artificial Intelligence
- Exposure to banking, finance, or regulated enterprise environments
- Experience optimizing models for low-latency inference
- Familiarity with UI-driven AI workflows (human-in-the-loop systems)
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