About this role
Summary: The Lead Consultant will design and build intelligent AI agents and multi-agent systems that combine autonomous AI with predictive analytics. The role involves developing agents capable of understanding business questions, analyzing enterprise data, executing analytical workflows, and delivering evidence-backed insi...
Job Summary
Role Summary
We are seeking an Agentic AI & Machine Learning Engineer to build intelligent systems that combine autonomous AI agents with predictive analytics. The role involves developing agents that understand business questions, discover and analyze enterprise data, execute analytical workflows, leverage machine learning models, validate results, and deliver evidence-backed insights.
The ideal candidate has strong expertise in Machine Learning, Statistics, Python, SQL, and LLM-powered agent development, with hands-on experience solving regression, classification, forecasting, anomaly detection, and decision-support problems in production environments.
Key Responsibilities
AI Agents & Multi-Agent Systems
- Design AI agents that plan, execute, validate, and explain multi-step analytical workflows.
- Build multi-agent systems for intent understanding, data discovery, tool selection, SQL/Python generation, model execution, and insight generation.
- Develop natural language interfaces for safe, reproducible analytics.
- Implement tool calling, memory, orchestration, structured outputs, human-in-the-loop controls, and agent evaluation frameworks.
- Ensure production readiness through guardrails, observability, validation, self-correction, retries, and access controls.
Machine Learning & Predictive Analytics
- Solve regression, classification, forecasting, anomaly detection, ranking, and optimization problems.
- Perform data preparation, feature engineering, feature selection, scaling, and leakage prevention.
- Build and evaluate models using Linear/Logistic Regression, Random Forest, Gradient Boosting, SVMs, and Neural Networks.
- Conduct cross-validation, hyperparameter tuning, error analysis, and model comparison.
- Apply explainability techniques such as SHAP and feature importance.
- Integrate ML models into agent workflows for automated inference and decision support.
Data Analytics & Intelligent Investigation
- Build NL-to-SQL and Python-based analytics capabilities.
- Develop reusable tools for trend analysis, segmentation, cohort analysis, hypothesis testing, anomaly detection, and visualization.
- Enable agents to perform descriptive, diagnostic, predictive, and prescriptive analytics.
- Deliver traceable insights with supporting evidence, confidence levels, and limitations.
Data, AI & MLOps Engineering
- Build scalable solutions using Python, SQL, APIs, databases, vector stores, and cloud AI services.
- Integrate structured/unstructured data using RAG where appropriate.
- Develop model training, deployment, monitoring, and retraining pipelines.
- Deploy solutions using FastAPI, Docker, Kubernetes, CI/CD, and cloud-native technologies.
Required Qualifications
- 3+ years of experience in AI Engineering, Machine Learning, Data Science, Analytics Engineering, or Software Engineering.
- Strong Python and SQL skills with experience working on production data systems.
- Strong foundation in machine learning, statistics, probability, and experimental design.
- Hands-on experience with regression and classification using Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or similar frameworks.
- Experience in model selection, tuning, validation, explainability, and translating results into business insights.
- Experience building LLM applications, AI agents, multi-agent systems, and NL-to-SQL solutions.
- Knowledge of agent orchestration, tool calling, RAG, prompt engineering, embeddings, and evaluation frameworks.
- Experience with Azure, AWS, or comparable cloud platforms and production deployment.
Preferred Qualifications
- Experience with LangGraph, LangChain, Semantic Kernel, AutoGen, or Microsoft Agent Framework.
- Experience with Azure OpenAI, Azure AI Search, Azure ML, AWS Bedrock, or SageMaker.
- Experience with MLflow, MLOps, model monitoring, and experiment tracking.
- Familiarity with PostgreSQL, SQL Server, Snowflake, Databricks, MongoDB, Redis, and vector databases.
- Experience with FastAPI, Docker, Kubernetes, CI/CD, RBAC, and secure AI applications.
Key Responsibilities
Key Responsibilities
AI Agents & Multi-Agent Systems
- Design AI agents that plan, execute, validate, and explain multi-step analytical workflows.
- Build multi-agent systems for intent understanding, data discovery, tool selection, SQL/Python generation, model execution, and insight generation.
- Develop natural language interfaces for safe, reproducible analytics.
- Implement tool calling, memory, orchestration, structured outputs, human-in-the-loop controls, and agent evaluation frameworks.
- Ensure production readiness through guardrails, observability, validation, self-correction, retries, and access controls.
Machine Learning & Predictive Analytics
- Solve regression, classification, forecasting, anomaly detection, ranking, and optimization problems.
- Perform data preparation, feature engineering, feature selection, scaling, and leakage prevention.
- Build and evaluate models using Linear/Logistic Regression, Random Forest, Gradient Boosting, SVMs, and Neural Networks.
- Conduct cross-validation, hyperparameter tuning, error analysis, and model comparison.
- Apply explainability techniques such as SHAP and feature importance.
- Integrate ML models into agent workflows for automated inference and decision support.
Data Analytics & Intelligent Investigation
- Build NL-to-SQL and Python-based analytics capabilities.
- Develop reusable tools for trend analysis, segmentation, cohort analysis, hypothesis testing, anomaly detection, and visualization.
- Enable agents to perform descriptive, diagnostic, predictive, and prescriptive analytics.
- Deliver traceable insights with supporting evidence, confidence levels, and limitations.
Data, AI & MLOps Engineering
- Build scalable solutions using Python, SQL, APIs, databases, vector stores, and cloud AI services.
- Integrate structured/unstructured data using RAG where appropriate.
- Develop model training, deployment, monitoring, and retraining pipelines.
- Deploy solutions using FastAPI, Docker, Kubernetes, CI/CD, and cloud-native technologies.
Required Qualifications
- 3+ years of experience in AI Engineering, Machine Learning, Data Science, Analytics Engineering, or Software Engineering.
- Strong Python and SQL skills with experience working on production data systems.
- Strong foundation in machine learning, statistics, probability, and experimental design.
- Hands-on experience with regression and classification using Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or similar frameworks.
- Experience in model selection, tuning, validation, explainability, and translating results into business insights.
- Experience building LLM applications, AI agents, multi-agent systems, and NL-to-SQL solutions.
- Knowledge of agent orchestration, tool calling, RAG, prompt engineering, embeddings, and evaluation frameworks.
- Experience with Azure, AWS, or comparable cloud platforms and production deployment.
Preferred Qualifications
- Experience with LangGraph, LangChain, Semantic Kernel, AutoGen, or Microsoft Agent Framework.
- Experience with Azure OpenAI, Azure AI Search, Azure ML, AWS Bedrock, or SageMaker.
- Experience with MLflow, MLOps, model monitoring, and experiment tracking.
- Familiarity with PostgreSQL, SQL Server, Snowflake, Databricks, MongoDB, Redis, and vector databases.
- Experience with FastAPI, Docker, Kubernetes, CI/CD, RBAC, and secure AI applications.
Skill Requirements
Key Responsibilities
AI Agents & Multi-Agent Systems
- Design AI agents that plan, execute, validate, and explain multi-step analytical workflows.
- Build multi-agent systems for intent understanding, data discovery, tool selection, SQL/Python generation, model execution, and insight generation.
- Develop natural language interfaces for safe, reproducible analytics.
- Implement tool calling, memory, orchestration, structured outputs, human-in-the-loop controls, and agent evaluation frameworks.
- Ensure production readiness through guardrails, observability, validation, self-correction, retries, and access controls.
Machine Learning & Predictive Analytics
- Solve regression, classification, forecasting, anomaly detection, ranking, and optimization problems.
- Perform data preparation, feature engineering, feature selection, scaling, and leakage prevention.
- Build and evaluate models using Linear/Logistic Regression, Random Forest, Gradient Boosting, SVMs, and Neural Networks.
- Conduct cross-validation, hyperparameter tuning, error analysis, and model comparison.
- Apply explainability techniques such as SHAP and feature importance.
- Integrate ML models into agent workflows for automated inference and decision support.
Data Analytics & Intelligent Investigation
- Build NL-to-SQL and Python-based analytics capabilities.
- Develop reusable tools for trend analysis, segmentation, cohort analysis, hypothesis testing, anomaly detection, and visualization.
- Enable agents to perform descriptive, diagnostic, predictive, and prescriptive analytics.
- Deliver traceable insights with supporting evidence, confidence levels, and limitations.
Data, AI & MLOps Engineering
- Build scalable solutions using Python, SQL, APIs, databases, vector stores, and cloud AI services.
- Integrate structured/unstructured data using RAG where appropriate.
- Develop model training, deployment, monitoring, and retraining pipelines.
- Deploy solutions using FastAPI, Docker, Kubernetes, CI/CD, and cloud-native technologies.
Required Qualifications
- 3+ years of experience in AI Engineering, Machine Learning, Data Science, Analytics Engineering, or Software Engineering.
- Strong Python and SQL skills with experience working on production data systems.
- Strong foundation in machine learning, statistics, probability, and experimental design.
- Hands-on experience with regression and classification using Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or similar frameworks.
- Experience in model selection, tuning, validation, explainability, and translating results into business insights.
- Experience building LLM applications, AI agents, multi-agent systems, and NL-to-SQL solutions.
- Knowledge of agent orchestration, tool calling, RAG, prompt engineering, embeddings, and evaluation frameworks.
- Experience with Azure, AWS, or comparable cloud platforms and production deployment.
Preferred Qualifications
- Experience with LangGraph, LangChain, Semantic Kernel, AutoGen, or Microsoft Agent Framework.
- Experience with Azure OpenAI, Azure AI Search, Azure ML, AWS Bedrock, or SageMaker.
- Experience with MLflow, MLOps, model monitoring, and experiment tracking.
- Familiarity with PostgreSQL, SQL Server, Snowflake, Databricks, MongoDB, Redis, and vector databases.
- Experience with FastAPI, Docker, Kubernetes, CI/CD, RBAC, and secure AI applications.
What Success Looks Like
A successful engineer will build analytical agents that can:
- Understand business questions and identify the right analytical or ML approach.
- Discover and validate relevant datasets and features.
- Generate and execute reproducible SQL/Python analyses.
- Train, select, and run predictive models.
- Explain predictions, investigate root causes, and test hypotheses.
- Present evidence-backed findings, confidence levels, limitations, and actionable recommendations.
Core Principle: Build intelligent agents that perform rigorous analytics and machine learning, not chatbots that simply summarize data.