Generative AI Engineer - Azure Cognitive Services (5-7 yrs)

Job Title : Generative AI Engineer.

Technical Skills :

- Total Experience 5+ years with hands on experience with building language models, machine learning and AI models leveraging industry tools, products, and Azure cognitive services.

- Deep and hands-on expertise in (as an individual contributor) Undertaking data collection, Data mining, preprocessing and analysis of structured and unstructured data.

- Hands-on expertise in large language models (LLMs/LSTMs/BERT) that can perform complex reasoning in few and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps e.

- Experience of building / customizing and fine-tuning AI models including LLM models via GenAI studios (OpenAI, Vertex or Bedrock) extended through Azure or AWS or GCP for rapid PoCs.

- Programming Skills - knowledge of statistical programming languages like R, Python, and database query languages like SQL, Hive, Pig is desirable.

- Familiarity with Scala, Java, or C++ is an added advantage.

- Deep and hands-on experience in applying machine learning algorithms.

- Strong data science and data engineering background both with open source and cloud distributed machines learning and AI tools especially Azure Cognitive Services and Azure Machine Learning.

- Strong understanding of cloud technology including Cognitive Services viz. Vision, Speech, Language, Decision, Search and its implementation.

- Proven experience with, and applied knowledge of, at least one scripting language, such as Python, or R.

- Deep expertise in building and fin-tuning large language models of OpenAI e.g GPT 4, GPT 3.5, DALL-E, Whisper, Embeddings, Moderation, and Codex etc.

- Expertise in completions, embeddings, edits, transcriptions, translations, fine-tunes, and moderations across the several large language models especially those of OpenAI, Bedrock or Vertex.

- Hands-on training and fine-tuning experience with distributed OpenAI models such as DaVinci, curie, Babbage, ADA.

- Must have delivered at least 1-2 projects leveraging GenAI services such as Azure OpenAI or Bedrock or Vertex services.

- Deep understanding of text / content representation techniques (such as n-grams, bag of words, sentiment analysis etc.), statistics and classification algorithms.

Behavioral Skills :

- Passionate about technology and customer success with excellent communication and articulation skills.

- Bring innovation by coming up ideas, differentiators, and development of tools / accelerators to differentiate us in the market.

- Support new business activities, proposal creation and engagement scoping.

- Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences.

- Research, curate, and record tutorials on various applications of data science and machine learning.

- Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences.

- Ability to work effectively in a dynamic, research-oriented group that has several concurrent projects.

Responsibilities :

- Independently build and demo rapid generative AI demos and solutions with short notices.

- Serve as lead data strategist/scientist/engineer to identify and integrate new datasets that can be leveraged through our product capabilities, and work closely with the engineering team in the development of data products with OpenAI (Azure), Bedrock (AWS) or Vertex (GCP).

- Build AI and ML models for generative AI applications with LLM models via OpenAI (Azure), Vertex (GCP) and Bedrock (AWS).

- Transforming natural language data into useful features using NLP/NLG/LLM techniques to feed classification algorithms.

- Work with several generative AI tools, stacks and products such as OpenAI (Azure), Bedrock (AWS) or Vertex (GCP) to quickly build and roll-out multiple use PoCs and MVPs.

- Identify valuable data sources and automate collection processes.

- Undertake preprocessing of structured and unstructured data.

- Analyze large amounts of information to discover trends and patterns.

- Build predictive models and machine-learning algorithms.

- Combine models through ensemble modeling.

- Present information using data visualization techniques.

- Propose solutions and strategies to business challenges.

- Collaborate with engineering and product development teams.