Job Description

Experience Required:
3-5 years of hands-on experience in full-stack development, system design, and supporting AI/ML data-driven solutions in a production environment.

Key Responsibilities

Implementing Technical Designs: Collaborate with architects and senior stakeholders to understand high-level designs and break them down into detailed engineering tasks. Implement system modules and ensure alignment with architectural direction. Cross-Functional Collaboration: Work closely with software developers, data scientists, and UI/UX teams to translate system requirements into working code. Clearly communicate technical concepts and implementation plans to internal teams. Stakeholder Support: Participate in discussions with product and client teams to gather requirements. Provide regular updates on development progress and raise flags early to manage expectations. System Development & Integration: Develop, integrate, and maintain components of AI/ML platforms and data-driven applications. Contribute to scalable, secure, and efficient system components based on guidance from architectural leads. Issue Resolution: Identify and debug system-level issues, including deployment and performance challenges. Proactively collaborate with DevOps and QA to ensure resolution. Quality Assurance & Security Compliance: Ensure that implementations meet coding standards, performance benchmarks, and security requirements. Perform unit and integration testing to uphold quality standards. Agile Execution: Break features into technical tasks, estimate efforts, and deliver components in sprints. Participate in sprint planning, reviews, and retrospectives with a focus on delivering value. Tool & Framework Proficiency: Use modern tools and frameworks in your daily workflow, including AI/ML libraries, backend APIs, front-end frameworks, databases, and cloud services, contributing to robust, maintainable, and scalable systems. Continuous Learning & Contribution: Keep up with evolving tech stacks and suggest optimizations or refactoring opportunities. Bring learnings from the industry into internal knowledge-sharing sessions. Proficiency in using AI-copilots for Coding: Adaptation to emerging tools and knowledge of prompt engineering to effectively use AI for day-to-day coding needs.

Technical Skills

Hands-on experience with Python-based AI/ML development using libraries such as TensorFlow, PyTorch, scikit-learn, or Keras. Hands-on exposure to self-hosted or managed LLMs, supporting integration and fine-tuning workflows as per system needs while following architectural blueprints. Practical implementation of NLP/CV modules using tools like SpaCy, NLTK, Hugging Face Transformers, and OpenCV, contributing to feature extraction, preprocessing, and inference pipelines. Strong backend experience using Django, Flask, or Node.js, and API development (REST or GraphQL). Front-end development experience with React, Angular, or Vue.js, with a working understanding of responsive design and state management. Development and optimization of data storage solutions, using SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra), with hands-on experience configuring indexes, optimizing queries, and using caching tools like Redis and Memcached. Working knowledge of microservices and serverless patterns, participating in building modular services, integrating event-driven systems, and following best practices shared by architectural leads. Application of design patterns (e.g., Factory, Singleton, Observer) during implementation to ensure code reusability, scalability, and alignment with architectural standards. Exposure to big data tools like Apache Spark, and Kafka for processing datasets. Familiarity with ETL workflows and cloud data warehouse, using tools such as Airflow, dbt, BigQuery, or Snowflake. Understanding of CI/CD, containerization (Docker), IaC (Terraform), and cloud platforms (AWS, GCP, or Azure). Implementation of cloud security guidelines, including setting up IAM roles, configuring TLS/SSL, and working within secure VPC setups, with support from cloud architects. Exposure to MLOps practices, model versioning, and deployment pipelines using MLflow, FastAPI, or AWS SageMaker. Configuration and management of cloud services such as AWS EC2, RDS, S3, Load Balancers, and WAF, supporting scalable infrastructure deployment and reliability engineering efforts.

Personal Attributes

Proactive Execution and Communication: Able to take architectural direction and implement it independently with minimal rework with regular communication with stakeholders Collaboration: Comfortable working across disciplines with designers, data engineers, and QA teams. Responsibility: Owns code quality and reliability, especially in production systems.
Problem Solver: Demonstrated ability to debug complex systems and contribute to solutioning.

Key: Python, Django, Django ORM, HTML, CSS, Bootstrap, JavaScript, jQuery, Multi-threading, Multi-processing, Database Design, Database Administration, Cloud Infrastructure, Data Science, self-hosted LLMs

Qualifications

Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, or a related field. Relevant certifications in cloud or machine learning are a plus.
Package: 6-11 LPA

Job Types: Full-time, Permanent

Pay: ?600,000.00 - ?1,100,000.00 per year

Work Location: In person