Job Location: Ahmedabad
Are you coming with strong Data Engineering background & also AI ML Engineering Excited about AI Adoption & Business Impact
This role is for you !
Has Scope to grow further and beyond ...
About the company
Company is an Accounting Firm, with International Client base. They are building a scalable, data-powered platform that transforms how businesses use information and automation. Not another dashboard project — we're connecting data across entire organisations and putting AI agents to work on it.
Early in this build and looking for a senior engineer who can own the Technical Direction.
What this role is really about
This is two jobs in one — and that's what makes it interesting.
First, you'll design and build the data infrastructure that everything else sits on — a secure, cloud-native data lake that ingests from multiple client and internal systems, transforms messy operational data into clean, trusted datasets, and makes it available to both analysts and AI agents in real time. You'll own the architecture decisions, the pipeline design, and the integrations that keep data flowing reliably between systems.
Second, you'll build the AI layer that sits on top. We're not talking about bolting a chatbot onto a website. We're building intelligent agents that query live data, surface insights, trigger automated workflows, and help people across the business make better decisions faster. You'll work with LLMs, vector databases, agentic frameworks, and real-time data to build AI systems that are genuinely useful — not just impressive in a demo.
The third piece is the one most job ads leave out: you'll drive AI adoption across the business. You'll spot processes in any department that could be transformed with AI tooling, prototype solutions, and help non-technical teams understand what's possible and actually start using it. This is evangelisation and change management as much as it is engineering.
The kind of work you'll do
- Design and deploy a cloud-based data lake with proper governance, security, and scalability built in from the start.
- Build ETL/ELT pipelines that connect accounting platforms, CRMs, ERPs, and third-party systems — turning raw operational data into clean, analytics-ready datasets.
- Develop APIs and integration layers that keep data flowing reliably between internal and external systems.
- Build AI agents and bots that can query unified data, generate insights, and act on information in real time — using LLMs, vector databases, and agentic orchestration frameworks.
- Design and implement RAG pipelines for semantic search and retrieval across large, complex datasets.
- Create dashboards and visualisations that make complex analysis accessible to non-technical stakeholders.
- Apply statistical and machine-learning techniques to improve forecasting accuracy, resource allocation, and business decision-making.
- Set up and manage CI/CD pipelines, cloud infrastructure, and DevOps practices so the platform runs at production grade.
- Champion AI adoption across the organisation — identify high-value use cases, build prototypes, run pilots, and help teams integrate AI tools into their daily workflows.
- Partner with business stakeholders to understand their needs, capture requirements, and translate them into data and AI solutions that actually get used.
What you'll bring
- 5+ years of professional experience across data engineering, backend engineering, or AI/ML engineering — ideally a mix.
- Strong Python and SQL skills — you've built production data pipelines and backend services, not just notebooks.
- Proven experience designing data lakes or data warehouses on a major cloud platform (AWS, GCP, or Azure).
- Hands-on experience with ETL/ELT tooling such as Airflow, dbt, or Prefect.
- Strong API development and systems integration experience — you've connected real-world, messy systems and made data flow reliably between them.
- Experience with LLMs, prompt engineering, and agentic frameworks (LangChain, LlamaIndex, CrewAI, or similar) — you've built AI systems that do real work, not just generate text.
- Working knowledge of vector databases and embedding pipelines for semantic search and retrieval.
- Practical, daily use of AI development tools — Claude Code, Cursor, GitHub Copilot, or similar. You're comfortable in a workflow where AI generates code and you review, guide, and iterate.
- Experience with microservices, event-driven architecture, and authentication/authorisation patterns.
- The ability to explain technical concepts to non-technical people clearly. You're a translator between what AI can do and what the business needs.
- Comfort with ambiguity — you don't wait for detailed specs. You figure out the right thing to build and then build it.
Send CV and Reply mail to: [Confidential Information];
Bonus points
- Strong Power BI capability (DAX, Power Query, Power Pivot) or experience with other BI/dashboarding tools.
- Experience building AI-driven SaaS products or client-facing AI tools.
- Familiarity with MCP integrations, agentic workflow orchestration, or building tools that AI agents use.
- MLOps experience, data governance frameworks, or real-time analytics pipelines.
- Background in accounting, financial services, or professional services.
Why this role is different
You're not maintaining someone else's legacy platform. You're building from the ground up — making the architecture decisions that define how the whole thing scales. The AI agents and automation that come out of your work will directly transform how businesses operate. And you won't just build tools and hand them over — you'll be in the room helping people use them, seeing the impact first-hand. If you want a role where the data engineering, the AI, and the business impact are all part of the same job, this is it.
Send updated CV to: [HIDDEN TEXT];
Will call you for detail discussion, if your Profile has relevant experience for the given combination role
