ML Ops Engineer ( Python Backend )

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ML Ops Engineer (Python Backend)

Engineering

Ahmedabad, Pune, Hyderabad

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About DataOrb

DataOrb is revolutionizing how organizations understand and utilize their customer data. We enable businesses of all sizes from ambitious startups to Fortune 500 companies to unlock insights from their customer interactions across conversational, transactional, and structured datasets. Founded by veterans from Google, Amazon, Microsoft, and Samsung, were driven by a shared mission to democratize customer intelligence and make AI accessible to everyone.

The Opportunity

We are seeking an experienced Python Developer proficient in object-oriented programming, Python development, cloud technologies, database design, and advanced Python concepts. The ideal candidate will have a foundational understanding of machine learning, with a strong willingness to learn and grow in this domain. The role involves writing high-quality Python code following SOLID principles and design patterns, as well as guiding and training team members to elevate their coding standards.

Core Responsibilities

• Architect and develop robust, scalable, and maintainable Python applications following microservice architecture principles

• Demonstrate proficiency in writing multithreaded and parallel processing code for optimizing performance

• Drive the creation of modularized codebase, ensuring reusability and maintainability across projects

• Develop high-quality Python code adhering to SOLID principles and design patterns

• Design and implement scalable solutions leveraging cloud technologies

• Contribute to database design and optimization strategies

• Mentor and guide team members to enhance code quality and best practices

• Collaborate with cross-functional teams to deliver robust and efficient solutions.

• Collaborate closely with stakeholders to understand requirements and translate them into technical solutions

• Drive code reviews and ensure adherence to coding standards, quality, and performance benchmarks

• Research and implement emerging technologies to enhance system efficiency

• Lead initiatives to improve development processes and tools, fostering innovation and productivity

• Foster a culture of continuous learning and improvement within the team

  Required Qualifications

• Full-time hands-on software engineering experience: Minimum 5+ years designing, developing, and delivering high-performance, production-grade applications using Python in complex, distributed systems.

• Expert-level proficiency in Python 3.12+ (or latest stable release):

• Deep understanding of object-oriented programming (OOP), design patterns (e.g., Singleton, Factory, Strategy), and Python-specific idioms (e.g., context managers, decorators, generators, async/await).

• Follows PEP8 standards and best practices for readable, maintainable, and testable code.

• Proficient in type hints (PEP 484), dataclasses, and pydantic for robust, type-safe applications.

• Strong grasp of core engineering concepts:

• Connection pooling (e.g., SQLAlchemy, psycopg2 connection pools)

• Scalability, throughput optimization, memory management, and profiling using tools like cProfile, line_profiler, memory_profiler.

• Asynchronous programming using asyncio, aiohttp, and event-driven architectures.

• Experience designing distributed systems and microservices using REST APIs, gRPC, or GraphQL.

• Cloud-native development experience, preferably with AWS (Lambda, SQS, SNS, S3, RDS, DynamoDB, SageMaker).

• Strong database expertise:

• Relational databases (PostgreSQL, MySQL): query optimization, indexing strategies, schema design, transactions.

• NoSQL databases (MongoDB 7+, DynamoDB): data modeling, query optimization, partitioning strategies.

• Proficient in testing methodologies:

• Unit testing (pytest, unittest), integration testing, mocking (unittest.mock, pytest-mock), and Test-Driven Development (TDD) practices.

• Familiarity with Testcontainers for integration tests in containerized environments.

• Containerization and orchestration:

• Proficiency with Docker (latest best practices, multi-stage builds, slim images).

• Experience with Kubernetes: deployments, services, config maps, secrets, and Helm charts.

• Experience guiding and mentoring team members code reviews, knowledge sharing, and promoting best practices across teams.

• Excellent communication and collaboration skills able to articulate design choices, trade-offs, and complex technical topics to both technical and non-technical stakeholders.

• Exposure to Machine Learning concepts:

• Familiarity with ML frameworks: TensorFlow 2.x, PyTorch 2.x, scikit-learn, or XGBoost.

• Experience integrating models into production services (model serving, feature stores, API wrappers).

• Hands-on experience with MLOps pipelines and tools:

• AWS SageMaker for model training, deployment, and monitoring.

• MLflow, Kubeflow, or ZenML for experiment tracking and reproducibility.

• Bonus: Experience with API design principles (OpenAPI/Swagger) and CI/CD pipelines for Python applications (GitHub Actions, GitLab CI, or Jenkins).

• Experience using Cloud ML platforms and MLOps frameworks in production environments, preferably AWS SageMaker

  Desired Experience

• Background in working on SaaS products

• Experience with AI/ML products

• Enterprise Python Engineer experience

  Educational Requirements

Bachelors Or Master s degree in one of the following fields:

• Bachelor of Computer Science

• Bachelor of Engineering (Information Technology)

• Masters of Computer Science

• Master of Engineering (Information Technology)

OR

Equivalent professional experience in Python Engineer (typically 4+ additional years of hands-on experience beyond the base requirement)

Technical Toolkit

• Python

• Django

• MongoDB

• Multithreading

• AWS

  Why Join DataOrb

• Mission: Be part of democratizing customer intelligence and making AI accessible

• Impact: Shape how organizations understand and serve their customers

• Team: Work with experienced leaders from top tech companies

• Growth: Rapid scaling environment with significant learning opportunities

• Culture: Autonomous, trust-based environment focused on outcomes

• Benefits:

• Flexible work arrangements

• Comprehensive health coverage

• Generous PTO policy

• Professional development support

• Competitive compensation package

  Our Values

• Customer Obsession: We practice what we preach

• Democratizing Technology: Making complex solutions accessible

• Innovation with Purpose: Solving real customer problems

• Trust and Autonomy: Freedom to create and deliver excellence

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Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.