Artificial Intelligence Engineer
Crescendo Global Leadership Hiring India
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Job summary
Crescendo Global Leadership Hiring India is seeking an Engineering Manager for AI/ML Innovation & Product Engineering in Pune. You will architect AI/ML and full-stack solutions, lead a growing tech team, and drive MVPs from concept to client-ready products.
You will own data pipelines, MLOps, security, and pilot deployments, collaborating with the Innovation Lab Head to align tech delivery with business goals. Based in Pune, you will interface with global clients and internal stakeholders.
Qualifications
- 8–10 years in software or ML engineering with 4+ years in technical leadership
- Experience building and deploying LLM-powered applications or autonomous AI agents
- Strong Python, API integration patterns, async architecture, and data pipelines
Responsibilities
- Architect end-to-end AI/ML and full-stack product architectures for the Lab
- Lead solutioning with global stakeholders to define AI roadmaps
- Enforce engineering standards, reviews, and documentation
- Lead hands-on development of AI agent prototypes and MLOps pipelines
- Integrate ERP APIs (SAP, Oracle, NetSuite) and document processing APIs
- Own pilot deployments with clients and translate findings into backlog
- Hire and grow the Lab tech team and manage sprint cadence
Skills
Education
Tools
Job description
Job Title
Engineering Manager, AI/ML Innovation & Product Engineering
Function
BPS Innovation Lab Technology Build Team
Reporting To
Innovation & Growth Lab Head
Location
India Pune
Team Size
Leads the Labs tech team (AI/ML + full-stack, headcount grows with hiring)
Grade / Level
Manager
Employment Type
Full-Time
The Engineering Manager, AI/ML Innovation & Product Engineering is the most critical technical hire in the Innovation Lab. This individual will design the architecture, build the initial versions, and establish the engineering standards for all AI-enabled and full-stack products developed by the Lab. Working closely with the Lab Head on commercial direction and leading the Labs tech team on execution, this role is the bridge between a validated business concept and a client-deployable product.
This role is ideal for a leader who is passionate about designing cutting-edge AI solutions, driving innovation, and delivering measurable business impact for global clients. The candidate should bring a builder’s mindset, hands-on expertise in AI/ML systems and Agentic AI, and the ability to translate complex client problems into scalable AI-powered solutions.
- Design the end-to-end technical architecture for all AI/ML and full-stack products built by the Lab, including agentic workflows, API integrations, service architecture, and inference infrastructure
- Lead solutioning discussions with global stakeholders to define AI-driven transformation roadmaps
- Define and enforce engineering standards, code review protocols, and documentation practices across the full tech team (AI/ML and full-stack)
- Evaluate and select the appropriate AI frameworks, cloud infrastructure, and third-party APIs (including LLM providers) for each product
- Ensure architecture decisions account for client data residency, security requirements, and third-party processing constraints from Day 1
- Own the IP integrity of all technical work — ensuring all code, models, and build artefacts are developed under ownership framework
- Own data pipelines for multi-modal and unstructured inputs — receipts, PDFs, vendor statements, contracts, email — across all Lab products
- Build OCR/layout-parsing ingestion and structured-extraction pipelines from unstructured sources
- Design and maintain embedding/vector-store architecture for RAG products; own retrieval quality
- Set data quality, labeling, and validation standards for evaluation and fine-tuning datasets
- Build reusable schema-on-read patterns for semi-structured ERP exports (SAP, Oracle, NetSuite)
- Build and own an evaluation/regression harness across all Lab products — accuracy, latency, cost-per-transaction, exception rate
- Govern prompt/model versions in production; roll back safely, catch provider-side regressions
- Lead hands-on development of AI agent prototypes, Gate 0 to Gate 1
- Build and maintain agentic workflows on LLM APIs (Claude, OpenAI, open-source models)
- Integrate financial ERP systems (SAP, Oracle, NetSuite), reconciliation platforms, document processing APIs
- Ship production-quality prototype code, not proof-of-concept demos
- Instrument per-MVP evaluation metrics: accuracy, latency, cost-per-transaction, exception rate
- Deploy MVPs into client pilot environments — secure data handling, access management, audit logging
- Diagnose and resolve technical issues during active pilots with the Lab Head and delivery team
- Turn pilot findings into product backlog items
- Prepare technical due-diligence materials for client security/procurement reviews
- Hire and grow the tech team (AI/ML and full-stack) as the Lab scales, staying hands-on in code review and architecture decisions
- Own sprint cadence — planning, prioritization, demos — aligned to Gate milestones
- Run performance management and career development for direct reports, anchored to technical growth; flag skill gaps to the Lab Head
- Allocate work across AI/ML and full-stack to maximize throughput without compromising quality or security
- Maintain a technology risk log: API dependencies, model performance, data handling, vendor concentration
- Own unit economics per product — API cost, infra cost, per-transaction margin
- Escalate client data usage, consent, or residency issues before go-live
- Ensure technology-partner arrangements are reflected in IP and vendor agreements
- 8-10 years of software engineering or ML engineering experience, including 4+ years in a technical leadership/team-lead capacity
- Production experience building and deploying LLM-powered applications or autonomous AI agents
- Strong Python; API integration patterns, async architecture, data pipeline design
- Built data pipelines for multi-modal/unstructured inputs — OCR, embeddings/vector stores for RAG, semi-structured extraction
- Integrated financial ERP APIs or document-processing pipelines (SAP, Oracle, NetSuite, or equivalent)
- Working knowledge of RAG architectures, prompt engineering, and LLM evaluation — has built evaluation/observability tooling, not just used it
- Demonstrable 0-to-1 build experience: concept to client-facing prototype
- Hired, managed, and developed a technical team spanning AI/ML and software engineering disciplines
- Full-stack development experience (React/TypeScript or equivalent) sufficient to review and unblock front-end/dashboard work
- Experience in Finance & Accounting process automation (AP/AR, reconciliation, expense management, contract management)
- Exposure to agentic frameworks: LangChain, LangGraph, AutoGen, CrewAI, or similar
- Experience with data residency, SOC 2, DPDP 2023, or client data handling requirements in an enterprise SaaS or BPO context
- Prior work in an outsourcing, shared services, or BPO technology environment
- Experience with MLOps/LLMOps tooling (LangSmith, Weights & Biases, MLflow, or equivalent) for evaluation and observability at scale
- Bachelor's or Master's degree in Computer Science, Engineering, or a quantitative field
- Relevant professional certifications (AWS/GCP/Azure AI, Databricks, or equivalent) are advantageous but not required
Comfortable with ambiguity; gets to a working artefact quickly and iterates. Produces code, not slides.
Designs for scale, security, cost, and observability from the start — not retrofitted after the demo.
Understands unit economics. Tracks cost-per-transaction and ties architecture decisions to margin.
Treats client data with production-grade rigor, including DPDP 2023 obligations. Zero tolerance for data-handling shortcuts.
Grows the team’s technical capability by staying hands-on: hires for real gaps, reviews code personally, unblocks rather than delegates. Not a people-manager-first profile.
Surfaces technical risks early and in plain language — does not manage upward by omission.
Handles unstructured and multi-modal data (documents, images, semi-structured exports) with the same rigor as structured data. Data quality and lineage are first-order, not afterthoughts.
The Engineering Manager, AI/ML Innovation & Product Engineering operates within a small team. The Lab is structured for speed and accountability — there are no large committees, no handoffs to a separate QA team, and no separation between 'build' and 'deploy.' The expectation is that this individual owns the technical outcome end-to-end.
Direct Reports
Lab tech team (AI/ML + full-stack engineers)
Reporting to
Innovation Lab Head (commercial lead)
Peer Relationships
BPS SMEs / Delivery Leads (domain input); US Hunter (client requirements translation)
Governance Interface
BPS CEO — escalation path for IP, data, and vendor risk issues
Technology Stack
Python, LLM APIs (Claude, OpenAI), agentic frameworks, financial ERP APIs, cloud infrastructure (AWS/GCP/Azure)
Key Dependencies
IP arrangement with technology partners must be resolved before development begins; client data consent framework must be in place before pilot deployment
Travel
Primarily India-based. Occasional travel to client sites or US for pilot support as required
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