Job Description

Job Summary

This role is responsible for architecting and designing advanced artificial intelligence and machine learning solutions that drive business transformation. The individual leverages deep expertise in scalable data platforms, distributed computing, and model development to deliver robust, secure, and high-performing systems. They provide strategic technical direction, mentor teams, and ensure alignment of technology strategies with organizational objectives.

Key Responsibilities

Key Responsibilities

1. Architect end-to-end AI/ML solutions using Python, TensorFlow, PyTorch, and scikit-learn, ensuring scalability, performance, and security across cloud and on-premise environments.
2. Design and implement distributed data processing pipelines with Apache Spark and Kafka, optimizing for real-time analytics and robust data ingestion.
3. Develop and operationalize machine learning models for NLP, deep learning, and time series forecasting using Spark Mllib, XGBoost, and LightGBM, ensuring seamless integration with PostgreSQL and DataBricks platforms.
4. Establish best practices for model deployment and monitoring using Apache Airflow and Bash scripting, enabling automated workflows and continuous delivery.
5. Mentor and guide technical teams in advanced AI/ML model development, fostering skill growth in Python, R, and SQL, and mitigating delivery risks through knowledge transfer.
6. Collaborate with stakeholders to gather requirements, define technology strategy, and align AI/ML architectures with evolving business needs and industry standards.
7. Architect and implement RESTful API integrations to enable seamless communication between AI/ML components and external systems, ensuring scalable, secure, and efficient data exchange across diverse enterprise environments.
8. Architect and implement RESTful API integrations to enable seamless communication between AI/ML components and external systems, ensuring scalable, secure, and efficient data exchange across diverse enterprise environments.

Skill Requirements

1. Expert proficiency in AI/ML model development, including classical ML, deep learning, NLP, and time series forecasting.
2. Excellent skills in Python, R, SQL, and Bash for data manipulation, model training, and automation.
3. Advanced expertise in TensorFlow, PyTorch, scikit-learn, pandas, NumPy, XGBoost, and LightGBM for building and deploying machine learning models.
4. Excellent knowledge of distributed computing and data engineering with Apache Spark, Spark Mllib, Apache Kafka, RabbitMQ, and DataBricks.
5. Solid understanding of relational databases, especially PostgreSQL and MySQL, for data storage and retrieval in ML workflows.
6. Expert ability to design secure, scalable architectures and implement best practices for AI/ML solution delivery.

Agentic AI, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), Large Language Modeling, Vector Databases.

Other Requirements

Skill Matrix – GenAI / ML (E3 Band)

The requirement is for a GenAI/ML professional with a total experience of 8+ years (Technical Experience) and 5+ years of relevant experience. The position is open across Pan India with preferred locations including Noida, Bangalore, Hyderabad, and Chennai. Candidates should be willing to attend office physically if required in these locations.

The project type is primarily development-oriented. The role is suitable for both individual contributors and those with team handling experience.

No shift work is involved in this role, and there will be no customer interview process. The sell rate is not applicable for this requirement.

The notice period is categorized as follows: Priority 1 includes candidates available within 0–30 days, Priority 2 includes those with 30–45 days, and Priority 3 includes candidates with 45–90 days.

The mandatory skills required include strong proficiency (Level 5) in Python and machine learning model development. Candidates must also have expertise in technologies such as Kafka, Spark, and PostgreSQL, along with hands-on experience in tools and frameworks like TensorFlow, PyTorch, Pandas, NumPy, and XGBoost.

In addition to the mandatory skills, the candidate should ideally have good-to-have proficiency (Level 4) in R, Bash scripting, scikit-learn, LightGBM, Spark MLlib, classical machine learning, deep learning, natural language processing, and time series forecasting. Exposure to Apache Spark, Apache Airflow, Apache Kafka, RabbitMQ, PostgreSQL, MySQL, and Databricks is also desirable.