Hyderabad, Telangana, India
Information Technology
Full-Time
Siemens
Overview
Gen AI Engineer
Job Description
Brightly Software is seeking a high performer to join our Product team in the role of Gen AI engineer to drive best in class client-facing AI features by creating and delivering insights that advise client decisions tomorrow.
As a Gen AI Engineer, you will play a critical role in building AI offerings for Brightly. You will partner with our various software Product teams to drive client facing insights to inform smarter decisions faster. This will include the following:
Job Description
Brightly Software is seeking a high performer to join our Product team in the role of Gen AI engineer to drive best in class client-facing AI features by creating and delivering insights that advise client decisions tomorrow.
As a Gen AI Engineer, you will play a critical role in building AI offerings for Brightly. You will partner with our various software Product teams to drive client facing insights to inform smarter decisions faster. This will include the following:
- Design and implement applications powered by generative AI (e.g., LLMs, diffusion models), delivering contextual and actionable insights for clients.
- Establish best practices and documentation for prompt engineering, model fine-tuning, and evaluation to support cross-domain generative AI use cases.
- Build, test, and deploy generative AI applications using standard tools and frameworks for model inference, embeddings, vector stores, and orchestration pipelines.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector stores like Pinecone, FAISS, or AWS OpenSearch
- Develop GenAI applications using Hugging Face Transformers, LangChain, and Llama related frameworks
- Perform exploratory data analysis (EDA), data cleaning, and feature engineering to prepare data for model building.
- Design, develop, train, and evaluate machine learning models (e.g., classification, regression, clustering, natural language processing) with strong exerience in predictive and stastical modelling.
- Implement and deploy machine learning models into production using AWS services, with a strong focus on Amazon SageMaker (e.g., SageMaker Studio, training jobs, inference endpoints, SageMaker Pipelines).
- Understanding and development of state management workflows using Langraph.
- Engineer and evaluate prompts, including prompt chaining and output quality assessment
- Apply NLP and transformer model expertise to solve language tasks
- Deploy GenAI models to cloud platforms (preferably AWS) using Docker and Kubernetes
- Monitor and optimize model and pipeline performance for scalability and efficiency
- Communicate technical concepts clearly to cross-functional and non-technical stakeholders
- Thrive in a fast-paced, lean environment and contribute to scalable GenAI system design
- Bachelor’s degree is required
- 2-4 years of experience of total experience with a strong focus on AI and ML and 1+ years in core GenAI Engineering
- Demonstrated expertise in working with large language models (LLMs) and generative AI systems, including both text-based and multimodal models.
- Strong programming skills in Python, including proficiency with data science libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and/or PyTorch.
- Familiarity with MLOps principles and tools for automating and streamlining the ML lifecycle.
- Experience working with agentic AI.
- Capable of building Retrieval-Augmented Generation (RAG) pipelines leveraging vector stores like Pinecone, Chroma, or FAISS.
- Strong programming skills in Python, with experience using leading AI/ML libraries such as Hugging Face Transformers and LangChain.
- Practical experience in working with vector databases and embedding methodologies for efficient information retrieval.
- Possess experience in developing and exposing API endpoints for accessing AI model capabilities using frameworks like FastAPI.
- Knowledgeable in prompt engineering techniques, including prompt chaining and performance evaluation strategies.
- Solid grasp of natural language processing (NLP) fundamentals and transformer-based model architectures.
- Experience in deploying machine learning models to cloud platforms (preferably AWS) and containerized environments using Docker or Kubernetes.
- Skilled in fine-tuning and assessing open-source models using methods such as LoRA, PEFT, and supervised training.
- Strong communication skills with the ability to convey complex technical concepts to non-technical stakeholders.
- Able to operate successfully in a lean, fast-paced organization, and to create a vision and organization that can scale quickly
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