Senior AI Engineer

Job Title: Senior AI Engineer

Location: Remote / Hybrid / Onsite

Employment Type: Full-Time

Department: Engineering / Artificial Intelligence

Experience Level: Mid-Level to Senior (5+ Years)

Reports To: Engineering Manager / AI Director

ABOUT THE ROLE

We are seeking a highly skilled and passionate AI Engineer to join our growing Artificial Intelligence team. The ideal candidate will be responsible for designing, developing, deploying, and maintaining cutting-edge AI solutions that drive innovation and business value.

As an AI Engineer, you will work closely with Software Engineers, Data Scientists, DevOps Engineers, Security Engineers, Product Managers, and Business Stakeholders to build scalable AI-powered applications. You will develop Machine Learning models, Large Language Model (LLM) applications, AI Agents, Retrieval-Augmented Generation (RAG) systems, and intelligent automation solutions that solve real-world business challenges.

The successful candidate will possess strong software engineering skills, practical AI/ML experience, cloud expertise, and a passion for emerging technologies in Generative AI.

KEY RESPONSIBILITIES

Artificial Intelligence Development

• Design, develop, and deploy AI-powered applications and services.

• Build and maintain machine learning models for production environments.

• Develop Large Language Model (LLM) applications using modern AI frameworks.

• Design and implement Retrieval-Augmented Generation (RAG) architectures.

• Build intelligent AI Agents and multi-agent systems.

• Develop conversational AI solutions such as chatbots and virtual assistants.

• Integrate foundation models into enterprise applications.

• Fine-tune open-source and proprietary language models.

• Evaluate model performance using appropriate metrics and benchmarks.

• Optimize AI systems for accuracy, reliability, scalability, and efficiency.

Software Engineering

• Write clean, maintainable, and scalable production-grade code.

• Develop RESTful APIs and microservices supporting AI applications.

• Collaborate with frontend and backend teams for seamless integration.

• Follow software development best practices and coding standards.

• Participate in architecture design and technical decision-making.

• Conduct code reviews and mentor junior engineers.

Machine Learning & Data Engineering

• Build data pipelines for training, validation, and inference workflows.

• Process structured and unstructured datasets.

• Develop feature engineering pipelines.

• Implement model training and evaluation workflows.

• Manage datasets and model versioning.

• Ensure data quality and integrity across AI systems.

Cloud & Infrastructure

• Deploy AI workloads on AWS, Azure, or Google Cloud Platform.

• Implement containerized solutions using Docker.

• Orchestrate AI applications using Kubernetes.

• Build Infrastructure as Code using Terraform.

• Create scalable AI inference environments.

• Monitor cloud resources and optimize infrastructure costs.

MLOps & Observability

• Implement CI/CD pipelines for machine learning applications.

• Automate model deployment and rollback procedures.

• Monitor model performance and drift.

• Build observability dashboards and alerts.

• Use monitoring tools such as Datadog, New Relic, Splunk, Prometheus, and Grafana.

• Maintain high availability and reliability of AI services.

AI Security & Governance

• Implement AI security best practices.

• Protect AI systems against prompt injection attacks.

• Prevent data leakage and unauthorized access.

• Perform AI threat modeling and risk assessments.

• Ensure compliance with privacy and security standards.

• Implement model governance and responsible AI practices.

• Conduct AI red teaming and security testing.

Research & Innovation

• Stay current with advancements in AI, Machine Learning, and Generative AI.

• Evaluate emerging AI tools, frameworks, and platforms.

• Prototype innovative AI solutions.

• Contribute to technical discussions and innovation initiatives.

• Share knowledge across engineering teams.

REQUIRED QUALIFICATIONS

Education

• Bachelor's Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Information Technology, or a related field.

• Master's Degree is a plus.

Experience

• 5+ years of professional software engineering experience.

• 2+ years of practical AI/ML development experience.

• Experience building and deploying production-grade applications.

• Experience working in cloud environments.

Programming Skills

• Strong proficiency in Python.

• Experience with JavaScript or TypeScript.

• Knowledge of Java, Go, or C# is a plus.

• Strong understanding of software engineering principles.

Machine Learning Knowledge

• Strong understanding of supervised and unsupervised learning.

• Understanding of deep learning concepts.

• Experience with neural networks.

• Knowledge of natural language processing (NLP).

• Familiarity with model evaluation techniques.

Large Language Models

• Understanding of transformer architecture.

• Experience working with LLMs.

• Knowledge of prompt engineering techniques.

• Experience implementing RAG systems.

• Experience integrating AI APIs.

Databases

• Experience with relational databases.

• Experience with NoSQL databases.

• Knowledge of vector databases.

Version Control

• Proficiency with Git.

• Experience working in collaborative development environments.

PREFERRED QUALIFICATIONS

• Experience with OpenAI APIs.

• Experience with Anthropic Claude APIs.

• Experience with Google Gemini APIs.

• Experience with open-source LLMs.

• Experience with LangChain.

• Experience with LangGraph.

• Experience with LlamaIndex.

• Experience with Hugging Face ecosystem.

• Experience building AI Agents.

• Experience implementing agent orchestration systems.

• Experience with AI Security.

• Experience with MLOps.

• Experience with distributed systems.

• Experience with event-driven architectures.

• Experience with cybersecurity practices.

TECHNICAL SKILLS

Programming Languages

• Python

• JavaScript

• TypeScript

• Java

• Go

• C#

Machine Learning Frameworks

• PyTorch

• TensorFlow

• Scikit-learn

• Keras

Generative AI

• OpenAI

• Anthropic Claude

• Google Gemini

• Hugging Face

• LangChain

• LangGraph

• LlamaIndex

Databases

• PostgreSQL

• MySQL

• SQL Server

• MongoDB

• Redis

• Pinecone

• Weaviate

• Chroma

• Milvus

Cloud Platforms

• Amazon Web Services (AWS)

• Microsoft Azure

• Google Cloud Platform (GCP)

DevOps & Infrastructure

• Docker

• Kubernetes

• Terraform

• GitHub Actions

• Jenkins

• GitLab CI/CD

Monitoring & Observability

• Datadog

• New Relic

• Splunk

• Prometheus

• Grafana

Security

• AI Security

• Application Security

• Cloud Security

• Identity and Access Management

• Threat Modeling

• Secure Development Lifecycle

SOFT SKILLS

• Excellent communication skills.

• Strong analytical thinking.

• Problem-solving mindset.

• Ability to work independently.

• Strong collaboration skills.

• Leadership and mentoring abilities.

• Adaptability and willingness to learn.

• Ability to explain technical concepts to non-technical audiences.

• Strong organizational skills.

• Growth mindset and continuous improvement attitude.

SUCCESS METRICS

• AI model accuracy and performance.

• System reliability and uptime.

• Inference latency and scalability.

• Cost optimization.

• Security compliance.

• User adoption and satisfaction.

• Successful deployment of AI initiatives.

• Reduced operational overhead through automation.

BENEFITS

• Competitive salary and bonus opportunities.

• Health, dental, and vision insurance.

• 401(k) retirement plan.

• Flexible work arrangements.

• Remote work opportunities.

• Professional development budget.

• Conference and certification sponsorship.

• Paid time off and company holidays.

• Career advancement opportunities.

• Access to cutting-edge AI technologies.