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Job Title: Product Lead or Tech Lead (AI & Infrastructure)

Location- Delhi

Job type: Full time, On site

About Us: TIMBLE is leading Authentication Company, delivering cutting edge technology and alternate data analysis for Identity management, Onboarding & Verification and Business Intelligence. We provide solutions across three verticals

1. BFSI Solutions

2. KYC and background check Solutions

3. AI Solutions

Role Overview-You will be the architectural backbone of Timble’s AI engine. This role requires a strong backend & systems mindset with exposure to AI/ML systems—balancing the development of high-accuracy fraud detection models with the scalable infrastructure required to run them.

Key Responsibilities

·      Engineering Leadership: Lead the development of our core AI products, including Bank Statement Analyzers, Face Match technology, and Electronic Residence Physical Verification (ERPV).

·      AI/ML Architecture: Design and deploy AI/ML-driven systems for document intelligence, fraud detection, and automation to enhance real-time intelligence.

·      Delivery Ownership: Take end-to-end ownership of features and ensure timely delivery in high-stakes production environments.

·      System Design & Scalability: Design and optimize high-throughput, low-latency API systems capable of handling real-world production loads across our 30+ high-quality APIs.

·      Hands-on Contribution: Remain hands-on with code when required, especially for critical modules, core architecture decisions, and troubleshooting.

·      Practical AI Application: Work on integrating and scaling AI/ML components in production. You must have the ability to apply complex AI solutions to solve real-world business problems.

·      Technical Strategy & InfoSec: Oversee Information Security protocols to protect proprietary financial data. Lead IP-related technical work, including patent-pending research for our authentication engines.

·      Mentorship: Act as the technical North Star for SDE-1 and SDE-2 engineers, instilling a culture of clean code, scalability, and cloud economics.

What We’re Looking For

·      Technical Expertise: Strong backend engineering expertise (Python or similar), with experience in building and maintaining scalable systems. Exposure to ML frameworks (TensorFlow/PyTorch) is a plus.

·      Domain Knowledge: Previous experience in Fintech, Cybersecurity, or BFSI tech stacks is highly preferred.

·      Infrastructure Skills: Solid experience with cloud infrastructure (AWS/GCP/Azure) and maintaining high availability.

·      Vision: The ability to translate complex fraud patterns into automated, executable code and a passion for "efficiency by design."

Learn more about us at: https://timbleglance.com

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, MLOps, and Generative AI. The ideal candidate will have hands-on experience in building scalable ML models, deploying them in production, and working with modern AI frameworks, including GenAI technologies.

Key Responsibilities

·      Design, develop, and deploy machine learning models for real-world business problems

·      Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring

·      Implement and manage MLOps pipelines for scalable and reproducible workflows

·      Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management

·      Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications

·      Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions

·      Optimize model performance and ensure production stability

·      Stay updated with the latest advancements in AI/ML and GenAI ecosystems

Required Skills & Qualifications

·      4+ years of experience in Data Science / Machine Learning

·      Strong programming skills in Python

·      Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.)

·      Solid understanding of MLOps practices and tools

·      Experience with MLflow or similar model lifecycle tools 

·      Practical experience in Generative AI (GenAI), including working with LLMs

·      Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch

·      Strong understanding of data structures, algorithms, and statistics

·      Experience with cloud platforms (AWS/GCP/Azure) is a plus

Good to Have

·      Experience with LLM fine-tuning, prompt engineering, or RAG pipelines

·      Exposure to Docker, Kubernetes, and CI/CD pipelines

·      Knowledge of data engineering workflows 

Review Criteria:

• Strong MLOps profile
• 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Must have hands-on Python for pipeline & automation development
• 4+ years of experience in AWS cloud, with recent companies
• (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

• Hands-on in Docker deployments for ML workflows on EKS / ECS
• Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
• Experience with CI / CD / CT using GitHub Actions / Jenkins.
• Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
• Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

• CV Attachment is mandatory
• Please provide CTC Breakup (Fixed + Variable)?
• Are you okay for F2F round?
• Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

• 8+ years in MLOps/DevOps with strong ML pipeline experience.
• Strong hands-on experience with AWS:
• Compute/Orchestration: EKS, ECS, EC2, Lambda
• Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow: MWAA/Airflow, Step Functions
• Monitoring: CloudWatch, OpenSearch, Grafana
• Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
• Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
• Strong Linux, scripting, and troubleshooting skills.
• Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

• Master’s degree in computer science, Machine Learning, Data Engineering, or related field.