Gurugram, Haryana
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
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