Lead Ai engineer

Job Description
Lead AI Engineer
Number of Openings
Location
1
Bhopal, Madhya Pradesh (On-site, Full-Time)
Reports To
Project Manager / Delivery Head
8+ Years
Full-Time, On-site
Experience Required
Employment Type
Domain
AI/ML, Financial Technology
Role Overview
We are looking for a hands-on Lead AI Engineer to anchor the technical delivery of a large-scale enterprise
AI/ML programme in the financial domain. This role owns end-to-end technical quality from solution
architecture and data pipelines through to model deployment and FMS integration. The Lead bridges strategy
and execution: guiding a team of data scientists, MLOps, RPA, and full-stack engineers; conducting design and
code reviews; and representing the technical team in all formal milestone reviews and client sign-offs.
Key Responsibilities
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Define and own the end-to-end solution architecture: AI/ML platform, MLOps pipeline, data
infrastructure (Data Lake/DW), RPA orchestration, and enterprise application integration layer.
Lead the technical delivery of multiple concurrent AI/ML workstreams spanning forecasting, anomaly
detection, risk scoring, document AI (OCR), classification, and conversational AI (chatbot).
Conduct hands-on code reviews, model validation reviews, and API design reviews across all
workstreams.
Lead technical requirement workshops with enterprise stakeholders; prepare and present architecture
and design documents for formal sign-offs.
Resolve cross-team dependencies between data engineering, MLOps, application, and integration
workstreams.
Define and enforce coding standards, SDLC documentation quality, and test coverage thresholds across
the team.
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Mentor and guide Senior Data Scientists, MLOps, Full-Stack, RPA, and Data Engineering team members.
Manage technical risks: data quality gaps, model accuracy issues, API instability, and security compliance.
Ensure all source code, model artefacts, and documentation are properly versioned and maintained in
SCM throughout delivery.
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Lead final handover, code walkthrough, and knowledge transfer at project close.
Required Skills & Experience
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8+ years of overall software/AI engineering experience; minimum 3-4 years in end-to-end AI/ML project
delivery.
Strong hands-on Python proficiency; deep understanding of ML fundamentals across supervised,
unsupervised, time-series, anomaly detection, and NLP domains.
Proven track record of delivering production-grade ML systems not just prototypes or PoCs.

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Solid data engineering background: ETL pipelines, Apache Spark, Airflow, Data Lake/DW architecture.
MLOps tooling experience: MLflow, Kubeflow or equivalent; CI/CD for ML models.
Familiarity with RPA platforms (UiPath / Robot Framework) and AI document processing pipelines.
Microservices architecture, REST API design, Docker/Kubernetes.
Experience with formal SDLC deliverables (SRS, HLD, LLD) and milestone-based delivery in large enterprise
or government/PSU programmes.
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Strong communication skills ability to present technical content clearly to non-technical stakeholders.
Preferred / Good to Have
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Prior experience in financial domain AI/ML: transaction monitoring, forecasting, vendor/credit risk
scoring.
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Experience with open-source LLMs and conversational AI platforms (Rasa, LLaMA-based models).
Knowledge of data security standards and IT governance in government or regulated enterprise
environments.
Qualifications
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B.Tech / M.Tech / M.Sc in Computer Science, AI/ML, Data Science, or related field.
Relevant certifications in ML, MLOps, or cloud infrastructure are a plus.