Md Marghub Akhtar — Lead ML Engineer

I am a Senior MLOps & AI Platform Engineer with 8+ years of experience designing, building, and operating enterprise-grade Machine Learning and Generative AI systems across regulated environments—pharmaceutical, retail, and cloud-native organizations. My focus is on end-to-end AI delivery: GenAI pipeline orchestration, RAG architectures, LLMOps, CI/CD automation for ML workloads, and observability/monitoring at scale. I work extensively with Azure OpenAI, LangChain, LangGraph, Databricks, Kubernetes, MLflow/Kubeflow, Docker, and Terraform to deploy production-ready LLM and ML systems that are secure, governed, and compliant. I have hands-on experience integrating vector databases, building agentic workflows, developing FastAPI-based microservices, and enabling scalable deployment patterns capable of handling thousands of internal inference requests per day. My work emphasizes governance—RBAC, secrets management, audit logging, and responsible AI guardrails—critical for regulated sectors such as healthcare and pharmaceuticals. Previously, I contributed to large-scale AI programs in GCC retail and healthcare, giving me strong exposure to emerging AI transformation initiatives across KSA and the Middle East. I thrive at the intersection of data science, platform engineering, and MLOps—bridging research prototypes and enterprise deployment. If your organization is exploring GenAI adoption, LLMOps maturity, platform engineering, or secure/regulated deployment patterns, I can help design and operationalize scalable solutions that balance performance, compliance, and business value. Core Competencies: MLOps • AI Platform Engineering • GenAI • RAG • LangChain • LangGraph • Azure OpenAI • Azure ML • Databricks • MLflow • Kubeflow • Terraform • Kubernetes • Docker • CI/CD automation • Responsible AI • Governance • Model Observability & Drift • Vector Databases

Stackforce AI infers this person is a Senior MLOps Engineer with expertise in AI platform engineering and deployment in regulated industries.

Location: Bengaluru, Karnataka, India

Experience: 8 yrs 2 mos

Skills

Mlops
Ai Platform Engineering
Data Science

Career Highlights

Expert in end-to-end AI delivery and MLOps.
Proven track record in Generative AI and scalable deployments.
Strong background in regulated environments like healthcare.

Work Experience

GSK

Lead MLOps Engineer (1 yr 5 mos)

Fractal

MLOps Engineer (2 yrs 8 mos)

Pluto7

Senior Data Scientist (1 yr 1 mo)

Data Scientist (1 yr 5 mos)

Cognizant

Programmer Analyst (1 yr 7 mos)

Education

Bachelor of Technology (B.Tech.) at DIT UNIVERSITY

Md Marghub Akhtar

Lead ML Engineer

Bengaluru, Karnataka, India8 yrs 2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in end-to-end AI delivery and MLOps.
Proven track record in Generative AI and scalable deployments.
Strong background in regulated environments like healthcare.

Stackforce AI infers this person is a Senior MLOps Engineer with expertise in AI platform engineering and deployment in regulated industries.

Contact

marghub79@gmail.com LinkedIn

Skills

Core Skills

MlopsAi Platform EngineeringData Science

Other Skills

AWS SageMakerAgentic AIAnalytical TechniquesApache AirflowApplied Machine LearningArtificial Intelligence (AI)Attention to DetailAzureAzure MLAzure OpenAIBusiness RequirementsBusiness UnderstandingCCI/CDCommunication

About

Experience

8 yrs 2 mos

Total Experience

2 yrs 3 mos

Average Tenure

1 yr 5 mos

Current Experience

Gsk

Lead MLOps Engineer

Jan 2025 – Present · 1 yr 5 mos · Bengaluru · Hybrid

Built Generative AI agents using LangGraph and LangChain with Azure OpenAI (GPT models) for multi-step
reasoning and agent workflows.
Enabled scalable deployment supporting thousands of internal requests per day with automated failover.
Developed autonomous and semi-autonomous agents for internal business use cases and evaluated Microsoft
Copilot Studio for enterprise agent development and governance.
Implemented GenAI pipelines integrating Databricks as the data source to support retrieval-augmented
generation (RAG) workflows using structured and unstructured data.
Worked on prompt engineering, including reusable Jinja-based prompt templates, prompt iteration, and
structured outputs to improve response consistency.
Developed REST APIs using FastAPI to expose GenAI services and built Streamlit-based user interfaces for
prototyping and internal validation.
Automated CI/CD for ML workloads using GitHub Actions and Terraform-driven infrastructure provisioning,
enabling secure multi-stage deployment pipelines for GenAI services.
Containerized applications using Docker and managed images through Azure Container Registry.
Developing a Gemini Enterprise + LangGraph multi-agent PoC on Google Cloud. (Ongoing)
Deployed and operated GenAI services on Microsoft Azure using Azure App Service, Azure ML, Azure Storage,
and Azure Key Vault for secure configuration management.
Integrated vector databases (FAISS) to support scalable retrieval for RAG-based agent workflows.
Established model observability and drift detection using Weave, Azure Application Insights, MLflow/Kubeflow
tracking, telemetry dashboards, and automated performance alerts.

LangGraphLangChainAzure OpenAIDatabricksCI/CDDocker+6

Fractal

MLOps Engineer

Apr 2022 – Dec 2024 · 2 yrs 8 mos · Bengaluru, Karnataka, India · Hybrid

Improved the efficiency of sales representatives by developing a Gen AI-powered chatbot using OpenAI, enabling faster preparation with up-to-date content for healthcare provider meetings.
Optimized LLM response evaluation methodologies, aligning them with strategic business objectives, which resulted in a significant boost in model accuracy and overall reliability.
Developed a user-item recommendation system on Azure cloud, optimized for mean average precision @K, driving a 20% increase in application traffic and significantly boosting revenue and customer base.
Engineered robust DAGs using Apache Airflow to replace Oozie workflows, reducing latency and minimizing failures in production environments.
Implemented a centralized feature store using the Feast library, significantly accelerating the deployment of machine learning models and enabling rapid iteration across multiple use cases.

OpenAIAzureRecommendation SystemsApache AirflowFeastMLOps

Pluto7

2 roles

Senior Data Scientist

Promoted

Feb 2021 – Mar 2022 · 1 yr 1 mo

Engineered demand forecasting models with a focus on minimizing Mean Squared Error (MSE), delivering precise sales predictions across multiple SKUs for an 8-week period. This initiative led to a 20% enhancement in inventory management and SKU planning.
Designed and deployed an object identification model on Google Cloud Platform, reducing operational costs by 5% and minimizing human intervention in manufacturing processes.
Developed a classification model to detect default agents based on historical performance, resulting in a 10% reduction in operational costs.
Enhanced user experience for a media agency by deriving actionable insights through NLP, significantly expanding the customer base.
Developed and deployed a loss ratio forecasting model leveraging 10 years of historical data, resulting in improved product outreach, capability, and a measurable increase in revenue.
Spearheaded the winning initiative in the Google Cloud Premier Partner 2021 contest, unlocking significant revenue opportunities and deepening strategic collaboration with Google Cloud.

Demand ForecastingNLPGoogle Cloud PlatformData Science

Data Scientist

Sep 2019 – Feb 2021 · 1 yr 5 mos

Cognizant

Programmer Analyst

Jan 2018 – Aug 2019 · 1 yr 7 mos · Bengaluru Area, India · On-site

** Delivered actionable insights for a retail cosmetics company, guiding strategic decisions in data collection, storage, and model development on Google Cloud Platform.