Lead or Associate Tech Architect - AI

Role & responsibilities

- Collaborating with cross-functional teams to define AI project requirements and objectives ensuring alignment with overall business goals

- Conducting research to stay up to date with the latest advancements in generative AI machine learning and deep learning techniques and identify opportunities to integrate them into our products and services.

- Optimizing existing generative AI models for improved performance scalability and efficiency.

- Active participation in developer communities, open-source contributions, or personal GenAI projects

- Work closely with the GenAI development team to integrate AI models and applications with the existing/new tool, quality, and performance.

- Act as an authority, guiding junior engineers and collaborating teams, fostering a culture of development excellence, collaboration, and continuous learning.

- Evaluating and selecting appropriate AI tools and machine learning models for tasks, as well as building and training working versions of those models using Python and other open-source technologies

- Working with leadership and stakeholders to identify AI opportunities and promote strategy.

- Serve as a technical lead, overseeing and supervising projects and engineers on the team.

- Play a leading hands-on role in the design and implementation of our ML infrastructure and GenAI platform software technologies.

- Collaborate in driving technology vision and roadmap.

- Establish software development best practices, and lead by example in applying them

- Experience contributing to the architecture and design of large scale distributed systems and/or ML systems and tools

- Hands-on experience leveraging large sets of structured and unstructured data to develop data-driven tactical and strategic analytics and insights using ML, NLP, computer vision solutions

- Strong sense of software design and usability of ML systems

- Experience applying software engineering methodologies and best practices including coding standards, code reviews, build processes, testing, and security.

- Proven experience as a Engineering Lead, hands on Software Engineering Manager, or similar role

- Develop natural language processing (NLP) solutions using GenAI, LLMs and custom transformer architectures.

- Adopt and customize LLM agent-based orchestration tools (LlamaIndex, Langchain, Semantic Kernel) for enterprise applications and use cases

- Implement information search and retrieval at scale, using a range of solutions ranging from keyword search to semantic search using embeddings.

- Developing and/or tune Large Language Models (LLM) and Generative AI (GAI)

- Analyze, design, develop, support and maintain Big Data environment and code base, support traditional data pipelines and processes.

Preferred candidate profile

- Proficient in Python, with experience in machine learning, deep learning, and NLP processing.

- Experience in developing and implementing generative AI models, with a strong understanding of deep learning techniques such as GPT, VAE, and GANs.

- Proficient in Langchain, LLM

- Prompt Engineering: The engineer prompts and optimizes few-shot techniques to enhance LLM's performance on specific tasks, e.g. personalized recommendations.

- Model Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.

- Response Quality: Collaborate with ML and Integration engineers to leverage LLM's pre-trained potential, delivering contextually appropriate responses in a user-friendly web app.

- It is essential to have a solid understanding of data structures, algorithms, and principles of software engineering.

- Experience with vector databases RDBMS, MongoDB and NoSQL databases.

- Proficiency in working with embeddings.

- Strong distributed systems skills and system architecture skills

- Experienced in building and running a large platform at scale.

- Hands-on experience with Python, Hugging Face, TensorFlow, Keras, PyTorch, Spark, or similar statistical tools.

- Experience as data modeling ML/NLP scientist. including, but not limited to, Performance tuning, fine-tuning, RLHF, and performance optimization. Validated background with ML toolkits, such as PyTorch, TensorFlow, Keras, Langchain, Llamadindex, SparkML, or Databricks.

- Proficient with the integration of data from multiple data sources Experience with NoSQL databases, such as HBase, ElasticSearch, and MongoDB API Design. API/Data mapping to schema.

- Experienced in and strong knowledge of using AI/ML and more particularly LLMs eager to apply this rapidly changing technology.

- Good Knowledge of Kubernetes, and RESTful design.

- Prior experience in developing public cloud services or open-source ML software is an advantage

Education : BE/B.Tech, ME/M.Tech, or graduation in relevant category

Perks and benefits

Competitive Salary

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