ML Engineer

Description :

We are seeking a highly skilled and experienced ML Engineer with 1216 years of experience in Machine Learning, Data Engineering, and Cloud-based AI solutions.

The ideal candidate will have strong expertise in AWS cloud technologies, production-grade ML systems, large-scale data pipelines, and Generative AI solutions including LLMs, RAG architectures, and agentic AI frameworks.

This role requires a hands-on engineering leader who can design, develop, deploy, and optimize scalable ML and GenAI applications in enterprise environments, preferably within highly regulated industries such as Financial Services.

Key Responsibilities :

- Design, develop, and deploy scalable machine learning and Generative AI solutions in production environments.

- Build and maintain enterprise-grade ML systems using AWS cloud-native services.

- Develop reusable frameworks and APIs for AI/ML model deployment and orchestration.

- Implement robust model training, evaluation, inference, and monitoring pipelines.

- Collaborate with data scientists, software engineers, and business teams to operationalize AI solutions.

AWS Cloud Engineering :

- AWS Lambda

- Amazon S3

- AWS Glue

- ECS/EKS

- AWS Step Functions

- Amazon Bedrock

- Amazon SageMaker

- Design scalable cloud-native architectures for AI/ML workloads.

- Optimize cloud infrastructure for performance, reliability, scalability, and cost efficiency.

- Implement serverless and containerized deployment strategies for ML applications.

- Develop and automate end-to-end data pipelines and ML workflows.

- Build scalable ETL/ELT pipelines using :

1. Spark

2. PySpark

3. AWS Glue

4. Apache Airflow

5. dbt

- Process and transform large-scale structured and unstructured datasets.

- Ensure high data quality, lineage, and reliability across enterprise data platforms.

- Integrate ML workflows into enterprise data ecosystems and downstream applications.

- Develop and implement LLM-powered applications and enterprise GenAI solutions.

- Apply prompt engineering techniques to optimize model performance and business outcomes.

- Design Retrieval-Augmented Generation (RAG) architectures for knowledge-driven AI applications.

- Implement context engineering, embedding pipelines, vector databases, and semantic search solutions.

- Work with fine-tuning approaches and custom model adaptation techniques.

Develop agentic AI workflows including :

- Tool calling

- Multi-agent orchestration

- Workflow automation

- Autonomous reasoning pipelines

- Evaluate and benchmark LLM performance using quantitative and qualitative evaluation methods.

Machine Learning & Analytics :

Apply traditional machine learning algorithms and statistical techniques including :

- Regression

- Classification

- Clustering

- Tree-based models

- Ensemble methods

- Perform exploratory data analysis (EDA) and feature engineering.

- Support experimentation, model validation, hyperparameter tuning, and model optimization.

- Develop monitoring frameworks for model drift, bias detection, and performance tracking.

DevOps, CI/CD & Engineering Best Practices :

- Implement software engineering best practices across AI/ML systems.

- Manage source code repositories using GitHub and Git workflows.

- Build CI/CD pipelines for automated testing, deployment, and release management.

Implement Infrastructure-as-Code (IaC) using :

- Terraform

- AWS CloudFormation

- Develop automated testing frameworks for data and ML systems.

- Build observability and monitoring solutions for production ML applications.

- Ensure AI/ML solutions comply with enterprise security, governance, and regulatory requirements.

- Implement secure data access controls, encryption, and compliance standards.

- Work within financial-services governance frameworks and audit requirements.

- Maintain documentation and operational standards for production AI systems.

Required Skills & Qualifications :

- 12 to 16 years of overall experience in software engineering, data engineering, or ML engineering.

- Strong hands-on experience building production systems on AWS cloud.

Expertise in :

- Python

- PySpark

- SQL

- Strong experience with distributed data processing and scalable data architectures.

- Experience automating data pipelines and machine learning workflows.

- Deep understanding of ML lifecycle management and MLOps principles.

AWS & Cloud Technologies :

- Lambda

- Glue

- SageMaker

- ECS/EKS

- Step Functions

- S3

- Bedrock

- Experience with containerization technologies such as Docker and Kubernetes.

- Familiarity with serverless architectures and cloud-native application development.

Generative AI & LLM Skills :

Hands-on experience with :

- Retrieval-Augmented Generation (RAG)

- Prompt Engineering

- LLM Evaluation

- Agentic Frameworks

- Context Engineering

- Fine-tuning techniques

- Experience integrating LLMs into enterprise workflows and applications.

- Knowledge of vector databases and semantic retrieval systems is highly preferred.

- Strong understanding of traditional ML methods :

1. Regression

2. Classification

3. Clustering

4. Decision Trees

5. Ensemble Models

6. Experience with EDA, feature engineering, and model evaluation techniques.

7. Familiarity with model serving, inference optimization, and monitoring.

Engineering & DevOps Skills :

Experience with :

- GitHub

- CI/CD pipelines

- Terraform

- CloudFormation

- Automated Testing

- Monitoring & Observability

- Strong understanding of software engineering principles and scalable system design.

- Excellent communication and stakeholder management skills.

- Ability to work independently in global and cross-functional teams.

- Strong analytical thinking and problem-solving capabilities.

- Ability to manage priorities and deliver high-quality solutions in fast-paced environments.

- Experience working in Financial Services, Banking, FinTech, or highly regulated industries.

- Exposure to enterprise AI governance and responsible AI frameworks.

- Experience with MLOps platforms and model governance tools.

- Knowledge of modern GenAI ecosystems and AI orchestration frameworks.

- AWS Certifications in Machine Learning or Solutions Architecture are a plus.

Educational Qualifications :

Bachelors or Masters degree in :

- Computer Science

- Data Science

- Artificial Intelligence

- Engineering

- Statistics

- Mathematics

- Certifications in AWS, ML Engineering, or Data Engineering are preferred.

Key Competencies :

- Machine Learning Engineering

- Generative AI & LLM Development

- AWS Cloud Architecture

- Data Pipeline Automation

- MLOps & CI/CD

- Distributed Data Processing

- AI Solution Architecture

- Problem Solving & Analytical Thinking

- Security & Compliance Awareness

- Collaboration & Communication