AI / ML Engineer

AI & Machine Learning Solution Development

• Partner with stakeholders to identify business challenges and translate them into scalable AI and machine learning solutions.
• Design, develop, fine-tune, and evaluate machine learning models, deep learning architectures, and Generative AI applications.
• Build intelligent solutions utilizing Large Language Models (LLMs), embeddings, Retrieval-Augmented Generation (RAG), vector search, and agent-based workflows.
• Prototype, test, and deploy AI-powered capabilities that improve decision-making and operational efficiency.
• Continuously evaluate emerging AI technologies and recommend innovative solutions.

Generative AI & LLM Engineering

• Develop enterprise-grade Generative AI applications using foundation models and modern AI frameworks.
• Design prompt engineering strategies and optimize model interactions for business use cases.
• Implement RAG architectures leveraging vector databases and semantic search technologies.
• Build AI agents and intelligent workflows that automate complex tasks and information retrieval.
• Fine-tune foundation models to improve domain-specific performance and business value.

Machine Learning Pipeline Engineering

• Design and implement end-to-end machine learning pipelines supporting model development, deployment, and monitoring.
• Build data ingestion, preprocessing, feature engineering, orchestration, and automated training workflows.
• Create scalable CI/CD frameworks for model, prompt, and pipeline deployment.
• Support repeatable, automated machine learning lifecycle processes.
• Ensure pipelines are reliable, maintainable, and production-ready.

Cloud AI Platform Development

• Develop and deploy AI solutions within cloud-native environments.
• Leverage cloud services for model training, deployment, storage, and analytics.
• Design scalable architectures that support enterprise-grade AI workloads.
• Integrate AI solutions with modern data platforms and cloud infrastructure.
• Support multi-environment deployment strategies that optimize performance and scalability.

Data Engineering & Analytics Collaboration

• Partner with data engineers and analytics teams to ensure data quality and accessibility.
• Support the design and implementation of data architectures that enable machine learning and AI initiatives.
• Build solutions for processing structured and unstructured data at scale.
• Collaborate on feature engineering, data transformation, and analytics optimization efforts.
• Ensure reliable delivery of high-quality datasets for model consumption.

MLOps & Production Operations

• Deploy machine learning and Generative AI workloads into production environments.
• Monitor model performance, drift, latency, and reliability.
• Establish observability, alerting, and operational governance for AI systems.
• Troubleshoot production issues and implement continuous improvements.
• Maintain robust operational processes that support long-term platform health.

Responsible AI, Security & Governance

• Implement Responsible AI principles, security controls, and compliance standards.
• Ensure models and data pipelines align with governance requirements and organizational policies.
• Support identity management, encryption, auditing, and access control frameworks.
• Promote ethical AI practices and transparency in model development.
• Mitigate bias and risk through testing, monitoring, and evaluation practices.

Experimentation & Model Optimization

• Conduct exploratory data analysis, experimentation, and model benchmarking.
• Perform hyperparameter tuning, model evaluation, and comparative analysis.
• Develop performance metrics that align technical outputs with business outcomes.
• Continuously optimize models to improve accuracy, efficiency, scalability, and cost effectiveness.
• Document findings, methodologies, and recommendations clearly.

Technical Leadership & Collaboration

• Provide technical guidance and mentorship to engineering and AI teams.
• Collaborate with product owners, platform teams, DevOps engineers, architects, and business stakeholders.
• Contribute to technical standards, architecture decisions, and best practices.
• Lead discussions around AI strategy, implementation approaches, and solution design.
• Communicate complex technical concepts effectively to both technical and non-technical audiences., * Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings & Vector Search
• AI Agents & Agentic Workflows
• Foundation Models & Fine-Tuning

Google Cloud Platform (GCP)

• Vertex AI
• Gemini APIs
• BigQuery
• Cloud Storage
• Identity & Access Management (IAM)
• Key Management Service (KMS)

Data Engineering

• Vertex AI Pipelines
• Dataflow
• Pub/Sub
• Feature Store
• Data Pipeline Development
• Data Integration & Transformation

MLOps & Infrastructure

• Model Deployment & Monitoring
• CI/CD Pipelines
• Kubernetes
• Containerized Workloads
• Microservices Architecture
• Production AI Operations

Search & Vector Technologies

• Vertex AI Search
• Vertex Vector Search
• Pinecone
• Milvus
• Semantic Search
• Knowledge Retrieval Architecture

Are you passionate about building cutting-edge AI solutions, leveraging Generative AI and machine learning to solve complex business challenges, and delivering secure, scalable cloud-based intelligence platforms that create meaningful impact?, * Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, or a related technical discipline.

• 3+ years of experience in Machine Learning Engineering, Data Science, AI Development, or related fields.
• Experience leading technical initiatives and providing technical direction across projects.
• Strong experience developing and deploying production-grade AI solutions.
• Ability to translate business challenges into scalable AI-powered capabilities.

Technical Skills

Programming & Analytics

• Python
• SQL
• Data Analysis & Feature Engineering
• Statistical Modeling
• Exploratory Data Analysis (EDA)

Machine Learning & AI

• TensorFlow
• PyTorch
• Scikit-Learn
• Deep Learning
• Supervised & Unsupervised Learning
• Model Evaluation & Optimization, * Master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related quantitative field.
• Experience supporting government, public sector, healthcare, or regulated industries.
• Familiarity with FedRAMP, NIST, HIPAA, or similar regulatory standards.
• Experience implementing Responsible AI frameworks and model governance practices.
• Exposure to AWS and Azure cloud ecosystems.
• Google Cloud Professional certification (Machine Learning Engineer, Data Engineer, or Cloud Architect).