JD Data Science / AI / ML Consultant – GenAI

Major Duties & Responsibilities

Work with business stakeholders and cross-functional SMEs to deeply understand business context, key business questions, and opportunities where GenAI and agent-based systems can augment decision-making and automation.

Create Proof of Concepts (POCs) / Minimum Viable Products (MVPs) including GenAI-powered solutions (e.g., LLM-based assistants, copilots, RAG systems), then guide them through to production deployment and operationalization.

Make solution recommendations that appropriately balance speed to market, analytical soundness, model autonomy, and human-in-the-loop controls.

Explore design options to assess efficiency and impact, including agent orchestration strategies, prompt strategies, tool-use patterns, and memory mechanisms to improve robustness and rigor.

Develop analytical / modeling solutions using a variety of commercial and open-source tools (e.g., Python, R, TensorFlow, LLMs, prompt frameworks, vector databases).

Formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations, optimization, and agentic workflows that plan, reason, and act autonomously within defined guardrails.

Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, stories, and AI system behavior explanations for business users.

Create algorithms and GenAI pipelines to extract, summarize, reason over, and generate insights from large, multi-parametric and unstructured data sets (text, documents, images).

Deploy ML and GenAI/Agentic AI systems to production to identify actionable insights from large databases, APIs, and enterprise systems.

Compare results from various methodologies including classical ML, deep learning, and GenAI approaches, and recommend optimal techniques based on accuracy, cost, latency, and risk.

Develop and embed automated processes for model and prompt evaluation, predictive model validation, deployment, monitoring, and drift detection (data, model, and prompt drift).

Work on multiple pillars of AI including data science, cognitive engineering, conversational AI, enterprise copilots, and multi-agent systems.

Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, reusability, reliability, and responsible AI compliance (bias, hallucination control, auditability).

Provide thought leadership and subject matter expertise in machine learning, GenAI, and Agentic AI, making impactful contributions to internal discussions on emerging practices, architectures, and governance.

Required Qualifications

Bachelor of Science or Bachelor of Engineering at a minimum.

5+ years of work experience as a Data Scientist / ML Engineer.

Strong combination of business focus, analytical thinking, and programming skills to rapidly cycle hypotheses, including GenAI experimentation and agent behavior tuning.

Advanced skills with statistical/programming software (e.g., Python, R) and data querying languages (e.g., SQL, Hadoop/Hive, Scala), plus experience integrating LLM APIs and AI services.

Strong hands-on skills in feature engineering, hyperparameter optimization, and prompt engineering / prompt evaluation techniques.

Experience producing high-quality, production-ready code, tests, and documentation, including AI system design artifacts.

Experience with Microsoft Azure or AWS data and AI platforms (e.g., Azure ML, Azure OpenAI, Databricks).

Strong understanding of descriptive and exploratory statistics, predictive modeling, ML algorithms, optimization, forecasting, deep learning, and GenAI system evaluation metrics.

Proficiency in statistical concepts, ML algorithms, and foundational GenAI concepts (transformers, embeddings, RAG, function/tool calling).

Good knowledge of Agile principles and iterative AI product delivery.

Ability to lead, manage, build, and deliver customer business results through data scientists or professional services teams, including GenAI solution delivery.

Ability to clearly communicate assumptions, AI limitations, risks, and results to both technical and non-technical stakeholders.

Self-motivated, proactive problem solver who can work independently and collaboratively in fast-evolving AI environments.