Senior Data Scientist – Gen AI Engineer - Assistant Vice President

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.

• 8–12 years of experience as a Data Scientist or equivalent role, with at least 2 years of specialized, hands-on experience in Generative AI, including leading technical development and mentoring teams.

• Demonstrable experience across the full lifecycle of production-level GenAI projects — from ideation and prototyping through deployment, monitoring, and ongoing maintenance in live environments. Proof-of-concept work alone is insufficient.

• Working with financial and enterprise data, applying modern NLP and GenAI techniques to solve business problems.

• Designing, refining, and systematizing prompt engineering strategies for large language models (LLMs), including structured prompting, chain-of-thought, and few-shot/zero-shot approaches.

• Collaborating with business stakeholders to translate requirements into GenAI-powered solutions.

• Developing, testing, and maintaining production-grade Python code for GenAI applications.

• Integrating with vector databases (e.g., Pinecone, Weaviate, Milvus, pgvector, Qdrant) for retrieval-augmented generation (RAG) pipelines.

• Building, monitoring, and optimizing MLOps/LLMOps pipelines for continuous model deployment and observability.

• Researching and evaluating emerging GenAI technologies, frameworks, and best practices to maintain competitive advantage.

• Troubleshooting and debugging GenAI models and agentic systems in production, including rapid identification and resolution of issues in real-world deployments.

• Communicating complex AI/ML concepts clearly to non-technical stakeholders, translating technical jargon into actionable business terms.

• Participating in and leading team meetings, design reviews, and architecture discussions.

Programming & Foundations

• Expert-level Python proficiency, including:

  • Core Python: data structures (lists, dictionaries, sets), algorithms, object-oriented programming, async programming, file handling, and exception handling.

  • Scientific computing: NumPy, Pandas, SciPy.

Machine Learning

• Scikit-learn, XGBoost, LightGBM.

• Strong understanding of advanced modeling techniques, model evaluation, hyperparameter tuning, and deployment strategies.

Deep Learning

• PyTorch (preferred/primary), TensorFlow/Keras.

• Familiarity with training, fine-tuning, and inference optimization for neural network architectures.

Generative AI (Updated for Current Landscape)

Area

Key Technologies & Concepts

LLM Frameworks:

Hugging Face Transformers, LangChain, LlamaIndex, Semantic Kernel

Agentic AI:

LangGraph, CrewAI, AutoGen, tool-use/function-calling patterns, multi-agent orchestration

LLM Architectures:

Transformer architectures (decoder-only, encoder-decoder), Mixture-of-Experts (MoE), multimodal models (vision-language models)

RAG (Retrieval-Augmented Generation):

Advanced RAG patterns (hybrid search, re-ranking, query decomposition, contextual retrieval), chunking strategies, embedding models (e.g., OpenAI, Cohere, open-source sentence-transformers)

Vector Databases

Pinecone, Weaviate, Milvus, Qdrant, pgvector, ChromaDB

Prompt Engineering:

Structured prompting, chain-of-thought, ReAct, few-shot/zero-shot, prompt chaining, guardrails and output parsing

Model Serving & Optimization

vLLM, TGI (Text Generation Inference), ONNX Runtime, quantization (GPTQ, AWQ, GGUF), model distillation

Evaluation & Observability

LLM evaluation frameworks (RAGAS, DeepEval, custom evals), LLM observability tools (LangSmith, Arize Phoenix, Weights & Biases), red-teaming and safety testing

API Development

FastAPI, RESTful and streaming API design for GenAI applications, WebSocket integration

Responsible AI

Bias detection and mitigation, content safety filters, hallucination reduction techniques, AI governance frameworks

MLOps / LLMOps

• CI/CD for ML/GenAI pipelines (e.g., GitHub Actions, GitLab CI).

• Experiment tracking and model registry (MLflow, Weights & Biases).

• Containerization and orchestration: Docker, Kubernetes.

• Infrastructure-as-code and deployment automation.

Cloud Platforms

• Proficiency in at least one major cloud platform's AI/ML services:

  • AWS (Bedrock, SageMaker, Lambda)

  • Azure (Azure OpenAI Service, Azure AI Studio, Azure ML)

  • GCP (Vertex AI, Gemini API)

• Excellent communication and collaboration skills — both written and verbal — with the ability to effectively convey technical concepts to diverse audiences, including senior leadership and business partners.

• Ability to articulate the challenges, trade-offs, and successes of deploying GenAI solutions at scale.

• Proactive approach to continuous learning in the rapidly evolving GenAI landscape.

• Master's or Ph.D. in a relevant field (Computer Science, AI/ML, NLP, or related).

• Experience with MLOps/LLMOps and building robust, automated AI pipelines at enterprise scale.

• Deep understanding of cloud-native architectures and their application in GenAI workloads.

• Experience developing and deploying conversational AI and agentic AI solutions in production environments.

• Contributions to open-source projects, research, or publications in the field of Generative AI or NLP.

• Experience building, curating, and managing large-scale datasets for training or fine-tuning GenAI models.

• Familiarity with graph databases (e.g., Neo4j) and knowledge graph integration with LLMs (GraphRAG).

• Experience with multimodal AI (text, image, audio, video).

• Bachelor's degree / University degree or equivalent experience.

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