Lead Ai engineer

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

-

-

- 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.

-

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.

Lead final handover, code walkthrough, and knowledge transfer at project close.
Required Skills & Experience

-

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.

-

-

-

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.

Strong communication skills ability to present technical content clearly to non-technical stakeholders.
Preferred / Good to Have

Prior experience in financial domain AI/ML: transaction monitoring, forecasting, vendor/credit risk
scoring.

- 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

- 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.