Apexon
AI Engineer
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About Apexon
Apexon is a digital-first technology services firm specializing in accelerating business transformation and delivering human-centric digital experiences. We help customers outperform their competition through speed and innovation, wherever they are in the digital lifecycle.
Apexon brings together core competencies in AI, analytics, app development, cloud, commerce, CX, data, DevOps, IoT, mobile, quality engineering, and UX — combined with deep expertise in BFSI, healthcare, and life sciences — to help businesses capitalize on the opportunities digital offers.
Backed by Goldman Sachs Asset Management and Everstone Capital, Apexon has a global presence of 15 offices (and 10 delivery centers) across four continents.
We enable #HumanFirstDigital
Role Overview
Key Responsibilities
- Build AI workflows for exception classification, triage, root-cause analysis, impact assessment, and remediation recommendations.
- Develop agentic workflows involving orchestration, tool/function calling, state management, and human-in-the-loop validation.
- Correlate new exceptions with historical issues, known root causes, business rules, and transaction/data attributes.
- Build Python and SQL-based services to query, transform, and analyze structured enterprise datasets.
- Integrate AI applications with enterprise APIs, databases, workflow/ticketing platforms, and internal data sources.
- Develop reusable tools and services that AI agents can invoke for retrieval, investigation, analysis, and workflow execution.
- Apply RAG/context retrieval where regulatory documents, historical knowledge, or issue repositories need to be searched.
- Implement confidence scoring, validation, guardrails, and traceability for AI-generated outcomes.
- Build backend services and APIs using Python/FastAPI.
- Implement testing, logging, evaluation, observability, exception handling, and production engineering practices.
- Collaborate with onshore AI engineers, architects, business analysts, data engineers, and application teams.
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Agentic AI Experience
- Hands-on experience with at least one agent/workflow orchestration framework such as LangGraph, Google ADK, CrewAI, AutoGen, Semantic Kernel, or an equivalent custom framework.
- Candidates should understand how to implement workflows such as: Input → Classification → Tool/Data Retrieval → Investigation → Reasoning → Validation → Action.
Data & Integration Experience
- Complex SQL and relational databases.
- Structured transaction, event, or operational data.
- JSON, REST APIs, and enterprise application integrations.
- Python-based data transformation and correlation across multiple datasets.
- Historical issue/event analysis and workflow/ticketing data.
- Handling incomplete, inconsistent, or changing enterprise data.
AI Engineering & Controls
- Known-vs-unknown or confidence-based classification.
- Semantic matching and contextual retrieval.
- Deterministic + LLM hybrid workflows.
- Human-in-the-loop workflows and approval gates.
- LLM/agent evaluation and output validation.
- Guardrails, audit trails, and observability.
Preferred Domain Experience
- Financial-services experience is preferred but not mandatory. Exposure to Regulatory Reporting, Capital Markets, trade lifecycle, post-trade processing, transaction reporting, reconciliations, exception management, risk, or controls will be advantageous. Familiarity with regulations such as EMIR, MiFID II, or SFTR is a plus.
Nice to Have
- Model Context Protocol (MCP) and reusable agent tool interfaces.
- Knowledge Graph / Graph RAG or data-lineage concepts.
- Vector databases, hybrid search, or re-ranking.
- AI observability and evaluation frameworks.
- Spec-Driven Development (SDD): ability to translate business/technical requirements into clear specifications, tasks, and acceptance criteria before implementation.
- Experience using Claude Code or equivalent AI coding assistants within disciplined software-engineering practices.
- Basic React or frontend integration experience.
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What We Are Looking For
We are looking for engineers who can build the complete workflow around an AI model—not simply prompts or basic chatbots. The candidate should be comfortable working across data, tools, agents, deterministic logic, validation, APIs, and production engineering.
A representative problem could involve receiving a new reporting exception across a large transaction population, determining whether it maps to a known issue, identifying potentially impacted transactions, establishing supporting evidence, and routing unresolved cases for further investigation.
Qualifications
- 5–8 years of professional experience in software engineering, AI/ML engineering, data engineering, GenAI, or related roles.
- Strong hands-on Python development experience.
- Demonstrated experience building LLM/GenAI applications beyond proof-of-concept chatbots.
- Practical experience implementing agentic workflows, tools, or orchestration.
- Strong analytical, debugging, and problem-solving skills.
- Ability to collaborate effectively across onshore/offshore engineering and business teams.
- Bachelor's or Master's degree in Computer Science, Engineering, AI, Data Science, or a related discipline preferred.
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