AI Solution Engineer

Aelum Consulting; Jaipur, Dadri; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Aelum Consulting is seeking a senior engineer-architect-lead to drive GenAI/agentic AI initiatives. You will design end-to-end architectures, lead a small engineering team, and stay hands-on in Python to build production-grade AI assistants and governance layers.

You will engage with clients, translate requirements into scalable solutions, and help shape pre-sales with compelling demos and robust technical narratives.

Qualifications

• 5–8 years in software/AI engineering with delivery of GenAI/agentic AI solutions in production or PoC.
• Strong Python and GenAI stack: RAG, orchestration, planning, tool usage, multi-step reasoning, prompt engineering, evaluation.

Responsibilities

• Solutioning & architecture: translate problems into target architectures with cost, security, latency trade-offs.
• Demos & client engagement: lead discovery, demos, PoCs, present architecture to technical and business stakeholders.
• Hands-on build (GenAI/agentic): develop and productionise GenAI components in Python, integrate LLMs with enterprise systems, stand up LLMOps.

Skills

GenAI / agentic AI

Python

LLM orchestration

RAG pipelines

Tool use & multi-step reasoning

Azure OpenAI

NLP / NLU

Tools

Azure OpenAI

Job description

About Aelum Consulting

Aelum Consulting is a ServiceNow Premier Partner helping enterprises accelerate their digital transformation journeys through innovation, automation, and scalable solutions. We specialize in delivering tailored ServiceNow implementations that drive efficiency, enhance user experience, and create measurable business impact across industries.

About the role

You'll be the technical face of our AI practice the person who turns a client's ambiguous "we want an AI assistant" into a credible, grounded, production-ready solution and then leads the team that builds it. Our work centers on enterprise conversational and agentic AI assistantsthat combine hybrid RAG(structured + document retrieval), LLM orchestration and query planning, deterministic business reasoning, and permission‑aware, auditable design.

Recent builds include agentic incident‑management agents and multi‑domain governance and contract‑intelligence assistants that reason over enterprise records and documents through a hybrid architecture spanning approved enterprise platforms and Azure OpenAI. This is a hybrid engineer–architect–lead role. You'll solution and pitch in pre‑sales, stay hands‑on in Python, and grow a small engineering team.

What you’ll do

Solutioning & architecture

• Translate client problems and RFPs into target architectures, weighing platform-native vs. hybrid orchestration vs. external‑assistant options and recommending with clear trade‑offs on cost, security, latency and governance.
• Design agentic and GenAI patterns end to end: intent/entity extraction, query planning, hybrid structured + document retrieval, deterministic calculation layers, grounded synthesis with citations, and human‑in‑the‑loop action execution.
• Apply "deterministic where you can, generative where you must" — keeping governed values (amounts, statuses, scores) computed or retrieved, never guessed.

Demos & client engagement

• Lead discovery: ask the right business, process and data questions before positioning a solution, and convert answers into scope, assumptions and effort.
• Build and deliver compelling demos and PoCs; present architecture and value convincingly to both technical and business stakeholders.
• Contribute to proposals — effort estimation, architecture narrative, licensing assumptions, acceptance criteria and ROI framing.

Hands‑on build (GenAI / agentic)

• Build and productionise GenAI/agentic components in Python: RAG pipelines, LLM orchestration, tool/function calling, prompt and evaluation harnesses, retrieval and ranking, and API integration.
• Integrate LLMs (Azure OpenAI and other enterprise‑approved models) with enterprise systems via approved APIs, ensuring RBAC, data‑residency and audit constraints are respected.
• Stand up LLMOps: prompt/model versioning, golden test sets, evaluation, telemetry, cost monitoring and release gates.

Team leadership

• Lead and mentor a Python engineering team — set technical direction, review designs and code, unblock, and raise the delivery bar.
• Own quality: grounding accuracy, RBAC/no‑leakage, hallucination control, and reliable action execution.

What you’ll bring (must‑have)

• 5–8 years in software/AI engineering, with recent, demonstrable delivery of GenAI / agentic AIsolutions in production or advanced PoC.
• Strong Pythonand the modern GenAI stack: RAG, LLM orchestration, agentic patterns (planning, tool use, multi‑step reasoning), prompt engineering, and evaluation.
• Practical experience integrating LLMs with enterprise systems and cloud AI services (Azure OpenAIor equivalent).
• Solid grasp of NLU/intent classification, vector search/embeddings, and structured‑vs‑unstructured retrieval design.
• Excellent communication and client‑facing presence— able to run discovery, present architecture, and demo credibly to mixed audiences.
• Proven ability to solution, estimate, and lead— you've owned technical scope and guided other engineers.
• Experience integrating AI into enterprise workflow platforms via APIs, events and middleware, with respect for platform RBAC, governance and audit controls.

Nice to have

• Experience with enterprise AI governance / responsible‑AI controls (RBAC‑aware retrieval, prompt‑injection mitigation, auditability, NIST AI RMF / OWASP LLM concepts).
• Exposure to open‑weight / self‑hosted LLMs and model portability strategies.
• Domain exposure to enterprise workflows (ITSM, CLM, vendor/risk, finance) that these assistants serve.
• Pre‑sales / consulting background in a partner or SI environment.