Senior Technical Lead

Job Summary

Senior AI Engineer Location: India, Gurgaon -Design, build, test, and deploy robust and scalable AI/ML models and applications. Take ownership of features from initial concept through to production and ongoing maintenance. -Contribute to the technical and architectural design of new AI systems and services, ensuring they meet standards for performance, security, and scalability. -Implement state-of-the-art machine learning and deep learning models. Fine-tune and optimize models for specific business use cases, focusing on accuracy and efficiency. -Analyze complex business requirements and translate them into well-architected, practical, and effective AI solutions. -Uphold high standards for code quality, testing, and documentation. Champion software engineering best practices within the team.  Deliver code that is secure, reliable and supportable. -Provide technical guidance and mentorship to junior engineers, assisting with code reviews and sharing knowledge to elevate the team's overall capabilities. -Demonstrate a strong curiosity for leveraging AI to improve personal and team productivity. Actively find and implement AI-powered tools and workflows to make your own role and development processes more efficient.  Effectively use AI code assistants to deliver code. -Ensure application diagrams and documentation stay current and relevant. -Adherence to agile development methodologies.   Key Accountabilities :  ·Collaboration with corporate technology teams on architectural designs. ·Partner with product teams to assist in roadmap initiatives and sequences. ·Participation in Agile ceremonies. Education •Bachelor’s in a relevant field of work or an equivalent combination of education and work-related experience.  Experience -Typically, a minimum of 6+ years of software engineering experience, progressive work-related experience with demonstrated proficiency in multiple disciplines, technologies, or processes related to the position. -Proven professional experience building and deploying software in a production environment. -Demonstrated experience in developing and deploying machine learning models or agentic applications. -Strong understanding of the full software development lifecycle, including testing, CI/CD, and monitoring. -Experience working with large datasets and complex data pipelines. -Ability to work effectively in a collaborative, agile team environment. -Experience working with a set of geographically dispersed team and bringing a holistic view of development projects. -An innate curiosity and a portfolio or history that demonstrates a commitment to continually learning new technologies as they evolve.   Technical Skill & Knowledge -Deep understanding of modern AI concepts, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows. -Proficiency in Python and extensive experience with its scientific computing and ML/DL libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch). -Strong theoretical and practical understanding of machine learning, deep learning, and natural language processing (NLP). -Hands-on experience with at least one major cloud provider (GCP, AWS, Azure) and their associated AI/ML services. -Familiarity with MLOps principles and tools for model versioning, deployment, and monitoring (e.g., Docker, Kubernetes, MLflow). -Experience with both SQL and NoSQL databases, and proficiency with data processing technologies like Spark is a plus. -Solid understanding of microservices architecture, API design (e.g., REST, gRPC), and containerization technologies (e.g., Docker, Kubernetes). -Strong analytical and problem-solving skills -Ability to display effective verbal and written communication skills when explaining complex technical issues to a variety of technical audiences, including clients, vendors, senior management and staff. -Direct experience with a major generative AI platform (e.g., Google Gem

Key Responsibilities

Design, build, test, and deploy robust and scalable AI/ML models and applications. Take ownership of features from initial concept through to production and ongoing maintenance.

-Contribute to the technical and architectural design of new AI systems and services, ensuring they meet standards for performance, security, and scalability.

-Implement state-of-the-art machine learning and deep learning models. Fine-tune and optimize models for specific business use cases, focusing on accuracy and efficiency.

-Analyze complex business requirements and translate them into well-architected, practical, and effective AI solutions.

-Uphold high standards for code quality, testing, and documentation. Champion software engineering best practices within the team.  Deliver code that is secure, reliable and supportable.

-Provide technical guidance and mentorship to junior engineers, assisting with code reviews and sharing knowledge to elevate the team's overall capabilities.

-Demonstrate a strong curiosity for leveraging AI to improve personal and team productivity. Actively find and implement AI-powered tools and workflows to make your own role and development processes more efficient.  Effectively use AI code assistants to deliver code.

-Ensure application diagrams and documentation stay current and relevant.

-Adherence to agile development methodologies.

Key Accountabilities :

 ·Collaboration with corporate technology teams on architectural designs.

·Partner with product teams to assist in roadmap initiatives and sequences.

·Participation in Agile ceremonies.

Skill Requirements

Deep understanding of modern AI concepts, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic workflows.

-Proficiency in Python and extensive experience with its scientific computing and ML/DL libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).

-Strong theoretical and practical understanding of machine learning, deep learning, and natural language processing (NLP).

-Hands-on experience with at least one major cloud provider (GCP, AWS, Azure) and their associated AI/ML services.

-Familiarity with MLOps principles and tools for model versioning, deployment, and monitoring (e.g., Docker, Kubernetes, MLflow).

-Experience with both SQL and NoSQL databases, and proficiency with data processing technologies like Spark is a plus.

-Solid understanding of microservices architecture, API design (e.g., REST, gRPC), and containerization technologies (e.g., Docker, Kubernetes).

-Strong analytical and problem-solving skills

-Ability to display effective verbal and written communication skills when explaining complex technical issues to a variety of technical audiences, including clients, vendors, senior management and staff.

-Direct experience with a major generative AI platform (e.g., Google Gemini, OpenAI).

Other Requirements

Work from office all days