Apply here :: https://www.betteranalytics.in/careers/?job-id=85c48776-863f-494c-873e-223414fcd5ba We are looking for a hands-on Senior AI/ML Engineer to help design and build our central enterprise intelligence platform—internally referred to as our “Mother System.”

The Mother System will integrate the organization’s core processes, data sources, applications, workflows, reports, analytics, and business rules into one intelligent ecosystem. It will use AI, machine learning, automation, natural-language interfaces, and predictive analytics to help businesses understand what is happening, identify what needs attention, and recommend or execute the next best action.

This is a foundational engineering role. You will work from architecture and data integration through model development, AI agents, APIs, deployment, monitoring, and continuous improvement.

The ideal candidate is a strong software engineer with deep AI/ML experience who can build production-grade systems—not just prototypes.

Key Responsibilities

Design and develop the architecture of the Mother System and its AI/ML components.

Build AI-powered services that connect enterprise processes, applications, data, and workflows.

Convert complex business requirements into practical AI/ML solutions.

Develop predictive models for forecasting, classification, anomaly detection, recommendations, optimization, and business intelligence.

Build generative AI applications using large language models, retrieval-augmented generation, tool calling, and intelligent agents.

Develop AI agents capable of retrieving information, reasoning over business context, and initiating approved workflows.

Integrate structured and unstructured data from ERP, CRM, finance, HR, operations, manufacturing, energy, and other enterprise systems.

Build robust data pipelines for ingestion, transformation, feature engineering, training, evaluation, and inference.

Design APIs, microservices, orchestration layers, and integration services for AI capabilities.

Develop reusable AI components rather than isolated solutions for individual customers.

Collaborate with software engineers to embed AI features into enterprise applications.

Establish model evaluation, testing, monitoring, observability, and rollback processes.

Improve system performance, reliability, accuracy, security, latency, and operating cost.

Work with business consultants and domain experts to validate model outputs and ensure practical usability.

Document architecture, models, datasets, prompts, experiments, integrations, and operational procedures.

Mentor junior engineers and contribute to engineering standards and technical decision-making.





AI/ML Responsibilities

Select appropriate models, algorithms, tools, and approaches for each business problem.

Build and deploy machine learning models using Python and modern ML frameworks.

Develop LLM-based systems with reliable grounding, source attribution, access controls, and evaluation.

Design RAG pipelines using enterprise documents, databases, APIs, and knowledge repositories.

Build semantic search, embeddings, vector retrieval, and knowledge graph capabilities.

Develop model evaluation frameworks covering accuracy, relevance, robustness, safety, latency, and cost.

Implement monitoring for data drift, model drift, hallucinations, quality degradation, and system failures.

Fine-tune or adapt open-source and commercial models where appropriate.

Create human-in-the-loop processes for sensitive, high-impact, or uncertain decisions.

Ensure AI outputs are explainable, auditable, and aligned with business rules.

Production AI systems require more than model development; they also need evaluation frameworks, MLOps, observability, scalable serving, and reliable integration with enterprise systems.careers.lululemon+1

Required Technical SkillsProgramming and Software Engineering

Strong proficiency in Python.

Robust understanding of object-oriented programming, data structures, algorithms, and software design principles.

Experience building production APIs and backend services.

Good knowledge of REST, authentication, authorization, asynchronous processing, and microservices.

Familiarity with Java and Spring Boot is an advantage.

Strong experience with Git, testing, debugging, code reviews, and technical documentation.

Machine Learning

Strong understanding of supervised and unsupervised learning.

Experience with regression, classification, clustering, forecasting, anomaly detection, recommendation systems, or optimization.

Experience with model training, feature engineering, validation, experimentation, and performance evaluation.

Hands-on experience with scikit-learn, PyTorch, TensorFlow, or equivalent frameworks.

Good understanding of statistics, probability, data quality, and experimental design.

Generative AI

Practical experience with LLM application development.

Experience with RAG, embeddings, vector databases, semantic search,



and prompt engineering.

Familiarity with tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar frameworks.

Experience integrating commercial or open-source models through APIs or self-hosted inference.

Understanding of agent orchestration, function calling, workflow automation, and guardrails.

Experience evaluating and improving LLM quality, latency, reliability, and cost.

Data and Integration

Strong SQL skills and experience with relational databases.

Experience working with structured and unstructured data.

Knowledge of data warehouses, data lakes, ETL/ELT pipelines, and data quality practices.

Experience integrating APIs, files, databases, event streams, and third-party enterprise applications.

Familiarity with Kafka, RabbitMQ, or similar messaging technologies is preferred.

Knowledge of graph databases or knowledge graphs is an advantage.

Cloud and MLOps

Experience deploying AI/ML systems on Azure, AWS, or Google Cloud.

Practical experience with Docker and containerized applications.

Familiarity with Kubernetes and CI/CD pipelines.

Experience with MLflow, Airflow, Kubeflow, or comparable tools.

Understanding of model serving, batch inference, real-time inference, logging, monitoring, and alerting.

Experience with infrastructure automation or cloud security is an advantage.

Qualifications

Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field.

5+ years of professional experience in AI/ML, machine learning engineering, data science, or applied artificial intelligence.

Demonstrated experience taking AI/ML solutions from experimentation to production.

Experience working with cross-functional teams and translating business problems into technical solutions.

Strong problem-solving, communication, ownership, and collaboration skills.

Experience mentoring engineers or leading technical initiatives is preferred.

What You Will Build

As part of this role, you may contribute to:

A unified business data and knowledge layer.

AI-powered enterprise search and question-answering systems.

Intelligent dashboards and decision-support tools.

Forecasting and predictive analytics engines.

Automated reporting and business insight generation.

Workflow automation through AI agents.

Process monitoring and anomaly detection.

Recommendation and next-best-action systems.

Domain-specific copilots for finance, operations, manufacturing, supply chain, energy, and management teams.

Secure APIs that allow internal and external applications to consume AI capabilities.

📌 Sr. engineer for AI/ML (Hyderabad)
🏢 Better Analytics
📍 Hyderabad