AI/ML Engineer

Years of Experience- 5-8 yrs

Location- Pune, Hyderabad, Chennai and Bangalore

Job Description

We are looking for an innovative and skilled AI/ML Engineer to design, build, and deploy AI integrations that leverage advanced machine learning models and cloud-native technologies. The role focuses on developing Python-based integration interfaces using FastAPI, interfacing with a variety of Large Language Models (LLMs), and deploying scalable solutions in AWSs EKS infrastructure. The successful candidate will collaborate with cross-functional teams to integrate AI capabilities into business workflows while ensuring reliability, security, and performance.

Key Responsibilities

Development & Integration

1. Build and maintain AI integration interfaces using Python FastAPI. 

2. Implement seamless connectivity with LLMs deployed on: 

   - Azure OpenAI Services. 

   - AWS Bedrock. 

   - Huggingface hosted models. 

3. Develop APIs to support integration with .NET-based web applications and ensure compatibility with various authentication mechanisms like OAuth 2.0. 

4. Collaborate with data scientists to deploy and operationalize AI/ML models within enterprise-grade solutions.

Cloud Infrastructure & Deployment

5. Deploy and manage applications in AWS EKS (Elastic Kubernetes Service). 

6. Implement AWS-native services such as DynamoDB, S3, and CloudWatch to support application scalability and resilience.

7. Set up and maintain CI/CD pipelines using Azure DevOps Pipelines to automate testing, deployment, and monitoring processes. 

8. Ensure high availability and fault tolerance for deployed systems, adhering to cloud best practices. 

Performance Optimization

9. Monitor application and model performance, identifying bottlenecks and implementing improvements for optimal efficiency and cost-effectiveness.

10. Optimize API and model performance for low latency and high throughput, particularly for real-time use cases.

Security & Compliance

11. Implement robust authentication and authorization mechanisms (e.g., OAuth) for secure data and model access.

12. Ensure compliance with relevant data privacy and security regulations, including GDPR and HIPAA, where applicable. 

13. Incorporate AI-specific guardrails for fairness, transparency, and ethical usage of models.

Collaboration & Communication

14. Work closely with Product, QA, and DevOps teams to deliver robust AI integrations aligned with business needs.

15. Document architecture, designs, and best practices for AI/ML integrations. 

16. Mentor junior engineers and contribute to building a strong engineering culture.

Qualifications

Technical Skills

1. Strong programming skills in Python, particularly for API development with FastAPI. 

2. Proficiency with Kubernetes and container orchestration on AWS EKS. 

3. Familiarity with integrating LLMs and AI models from: 

   - OpenAI, Azure OpenAI, AWS Bedrock, Huggingface. 

4. Experience with AWS Services: DynamoDB, Lambda, S3, CloudWatch.

5. Strong understanding of authentication protocols, especially OAuth 2.0 and integration with .NET web apps. 

6. Experience with CI/CD pipelines using Azure DevOps Pipelines and version control with Git. 

7. Familiarity with SQL and NoSQL databases, particularly DynamoDB and relational databases. 

8. Knowledge of AI/ML workflows and model serving frameworks such as TorchServe, TensorFlow Serving, or equivalent.

9. Strong debugging and performance profiling skills for APIs and AI model integrations. 

10. Awareness of AI governance principles like fairness, bias mitigation, explainability, and compliance. 

Soft Skills

1. Problem-solving mindset with attention to detail and analytical skills. 

2. Excellent communication skills to interact with diverse teams and explain technical concepts effectively. 

3. Adaptability to work in a dynamic environment with evolving project requirements. 

4. Strong organizational skills to prioritize tasks and meet deadlines in fast-paced settings. 

Preferred Qualifications

1. Experience with AI/ML platforms and tools such as MLFlow, Kubeflow, or AWS SageMaker. 

2. Exposure to .NET frameworks and their integration with AI applications. 

3. Knowledge of adversarial testing and robustness evaluation for AI models. 

4. Certifications in cloud platforms like AWS (e.g., AWS Certified Solutions Architect) or AI/ML (e.g., AWS Certified Machine Learning, Microsoft AI Engineer). 

5. Familiarity with ethical AI frameworks and standards such as NISTs AI Risk Management Framework.

Key Performance Indicators (KPIs)

1. Uptime and reliability of deployed AI integrations. 

2. Reduction in API latency and model response time. 

3. Successful delivery of CI/CD pipelines for AI workflows. 

4. Adherence to AI guardrails and ethical guidelines in deployed solutions. 

5. Effective collaboration and mentorship within the engineering team.