AIML Senior Developer

Must Have Skills Generative AI GPT3 ML Ops and Python

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 fewshot techniques to enhance LLMs performance on specific tasks e.g. personalized recommendations.

Model Evaluation & Optimization: Evaluate LLMs zeroshot and fewshot capabilities finetuning hyperparameters ensuring task generalization and exploring model interpretability for robust web app integration.

Response Quality: Collaborate with ML and Integration engineers to leverage LLMs pretrained potential delivering contextually appropriate responses in a userfriendly 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.

Handson 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 finetuning 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 opensource ML software is an advantage

hbase,llamadindex,spark,mongodb,rdbms,gpt3,databricks,rlhf,gans,elasticsearch,vae,kubernetes,keras,sparkml,langchain,tensorflow,pytorch,embeddings,python,nosql,gpt,ai/ml,performance optimization,restful design,machine learning,deep learning,nlp processing,hugging face,ml ops,software engineering,ai models,performance tuning,data structures,llm's pre-trained potential,system architecture skills,open-source ml software,distributed systems skills,developing public cloud services,llm's zero-shot and few-shot capabilities,building and running a large platform