Senior AI/ML Engineer

Job Summary

We are seeking a highly skilled AI/ML Engineer to design, develop, and deploy scalable Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) solutions that address complex business challenges. The ideal candidate will have extensive experience building production-grade AI systems, developing LLM-powered applications, and integrating AI capabilities into enterprise platforms. This role requires expertise across the AI/ML lifecycle, cloud technologies, MLOps/LLMOps practices, and modern Generative AI frameworks.

Key Responsibilities

AI & Machine Learning Development

• Design, develop, and implement AI, Machine Learning, and Generative AI solutions for business use cases.
• Analyze business problems and recommend appropriate AI approaches, including ML models, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, prompt engineering, and cloud AI services.
• Develop, evaluate, deploy, and continuously improve machine learning models and AI-driven solutions.
• Build scalable AI/ML pipelines for data processing, model training, validation, deployment, monitoring, and retraining.

Generative AI & LLM Solutions

• Build and integrate LLM-based applications using prompt engineering, orchestration frameworks, semantic search, vector databases, and model evaluation techniques.
• Develop and implement RAG architectures leveraging embeddings and vector search technologies.
• Evaluate and optimize GenAI applications for quality, accuracy, latency, security, scalability, and cost efficiency.
• Monitor and address hallucinations, model drift, and performance degradation in production environments.

Cloud & Platform Integration

• Integrate AI/ML and GenAI solutions into enterprise applications, APIs, workflows, and business platforms.
• Utilize cloud-based AI platforms and data ecosystems such as Microsoft Azure, AWS, Google Cloud Platform (GCP), Databricks, Kubernetes, and serverless services.
• Design and deploy scalable, secure, and reliable AI solutions within modern cloud architectures.

MLOps & LLMOps

• Apply MLOps and LLMOps best practices, including CI/CD pipelines, model versioning, experiment tracking, deployment automation, and performance monitoring.
• Establish automated testing, retraining, and governance processes for AI solutions.
• Ensure model reliability, scalability, and operational excellence across AI deployments.

Responsible AI & Governance

• Implement responsible AI practices, including data privacy, explainability, bias mitigation, access controls, prompt safety, and regulatory compliance.
• Ensure alignment with organizational governance, security, and risk management requirements.

Collaboration & Leadership

• Communicate complex AI/ML concepts and analytical findings to technical and non-technical stakeholders.
• Partner with engineering, product, business, and data teams to deliver impactful AI-driven solutions.
• Serve as a technical resource and mentor to junior team members.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field.
• 7+ years of experience in Software Engineering, Data Science, Machine Learning, AI Engineering, or related technical disciplines.
• 5+ years of hands-on experience developing, deploying, and maintaining machine learning models and AI solutions in production environments.
• Strong experience in Generative AI solution design and implementation.

Required Technical Skills

• Hands-on experience with:
  • Large Language Models (LLMs)
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • Embeddings and Semantic Search
  • Vector Databases
  • Model Evaluation and Optimization
• Strong programming skills in Python and SQL.
• Experience developing REST APIs and integrating AI services into enterprise applications.
• Proficiency with AI/ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, LangChain, and similar technologies.
• Strong understanding of the end-to-end machine learning lifecycle.
• Experience with MLOps and LLMOps frameworks and tooling.

Cloud & Platform Experience

Hands-on experience with one or more of the following:

• Microsoft Azure AI Services
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)
• Databricks
• Kubernetes
• Serverless architectures
• Vector database platforms such as Pinecone, Weaviate, ChromaDB, or Azure AI Search

Preferred Qualifications and Competencies

• Experience in Natural Language Processing (NLP), Natural Language Understanding (NLU), Deep Learning, Computer Vision, or Speech Recognition.
• Experience working with large-scale distributed computing and data processing frameworks.
• Knowledge of AI governance, security, privacy, and responsible AI practices.
• Exposure to research-oriented AI development and applied AI innovation.
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in ambiguous and complex environments.
• Strong collaboration and leadership capabilities.
• Passion for emerging AI technologies and continuous learning.