Key Responsibilities

1. Design, develop, and maintain advanced AI/ML/GenAI applications, ensuring robust performance and scalability.
2. Drive the end-to-end pipeline for ML development and deployment, encompassing data collection, data analysis, model training, validation, deployment, and integration.
3. Apply expertise in LLMs, including prompt engineering and the utilization of vector databases for efficient information retrieval.
4. Leverage containerization technologies such as Docker and Kubernetes, along with cloud platforms like Google Cloud Platform (GCP), for scalable and efficient deployment.
5. Lead and guide junior engineers, conduct code reviews, facilitate planning sessions, and provide technical mentorship to foster team growth and excellence.
6. Utilize AI and ML techniques to accelerate and optimize software development cycles, enhancing productivity and efficiency.
7. Collaborate effectively with cross-functional teams to define, design, and implement new features and functionalities.
8. Write clean, efficient, and well-documented code, adhering to high-quality coding standards and best practices.
9. Troubleshoot and debug software issues, ensuring the stability and reliability of deployed applications.
10. Conduct research and experimentation to explore new machine learning approaches and technologies.
11. Contribute to the continuous improvement of software development processes, methodologies, and best practices.
12. Educational Qualifications
13. Bachelor's or Master's degree in Computer Science, Engineering, Data Science,



or a related quantitative field.
14. Equivalent practical experience will also be considered.

Must-Have Skills

1. Strong logical reasoning and analytical problem-solving skills.
2. In-depth understanding of Data Structures and Algorithms (DSA).
3. Proficiency in Machine Learning (ML) concepts and algorithms.
4. Expertise in Natural Language Processing (NLP) and Large Language Models (LLMs).
5. Solid foundation in Deep Learning architectures and frameworks.
6. Proven experience in end-to-end ML development, deployment and monitoring pipelines.
7. Experience with LLM applications, including prompt engineering and vector databases.
8. Experience with DevOps and CI/CD practices for ML pipelines.
9. Hands-on experience with containerization technologies (Docker, Kubernetes).
10. Experience with cloud platforms (e.g., AWS, Azure, GCP).
11. Demonstrated ability to lead and guide technical teams, including code reviews and planning sessions.
12. Excellent communication, leadership, and problem-solving skills.

Positive-to-Have Skills

1. Experience in backend development and integration using technologies such as FastAPI, .NET.
2. Familiarity with NoSQL databases like MongoDB, and search engines like Elasticsearch.
3. Experience with message queuing systems like RabbitMQ.
4. Experience with SLM fine-tuning and PEFT techniques like LoRA, QLoRA, etc.
5. Experience in agentic application development.

Education:

Bachelors degree in a technical discipline

Experience Level: As a Technical Lead - 4-7 years of experience

📌 ML Technical Lead (Bengaluru)
🏢 TESCRA
📍 Bengaluru