Specialty Developer V

Specialty Developer V (AI Engineer / MLOps Lead) In this senior technical role, you will be responsible for designing, building, and deploying cutting-edge AI/ML solutions that enhance enterprise business processes, improve decision-making, and drive advanced automation. Combining software engineering rigor, data science, and machine learning expertise, you will build scalable AI systems in production environments. You will develop end-to-end data pipelines, fine-tune Large Language Model (LLM) applications using modern frameworks (such as LangChain, LangSmith, and OpenAI APIs), and engineer scalable microservices to serve models in real time. Working within a collaborative Agile environment, you will partner closely with data engineering, cloud, and product teams to translate complex business problems into high-impact AI solutions. Duration: 12-Month Contract (with high potential for extension) Work Arrangement: Hybrid (4 days per week on-site at the corporate office, 1 day work from home; flexible anchor days) Advantages - Cutting-Edge Generative AI Scope: Architect, fine-tune, and deploy enterprise-grade LLMs, vector embeddings, and multimodal AI solutions directly into production. - Production MLOps Sandbox: Build end-to-end machine learning pipelines, real-time microservices, and continuous monitoring frameworks for model drift and performance. - 12-Month Contract Stability: Secure a full-year contract engagement within a well-funded, top-tier enterprise technology organization. - High Strategic Impact: Bridge the gap between data science research and enterprise delivery, presenting innovative AI capabilities to key internal stakeholders. Responsibilities AI/ML Pipeline Engineering & LLM Development - End-to-End Pipeline Development: Design and deploy comprehensive ML pipelines encompassing data preprocessing, feature engineering, model validation, and automated production deployment via CI/CD. - LLM & Generative AI Solutions: Fine-tune and build LLM-based applications using advanced frameworks such as LangChain, LangSmith, and OpenAI APIs. - Microservices & API Architecture: Build scalable, real-time microservices and RESTful APIs to serve ML models within production environments. - Model Governance & Monitoring: Implement continuous monitoring frameworks to detect model drift, track performance metrics, and ensure reliability, data security, and data governance. Collaboration & Strategic Innovation - Cross-Functional Partnership: Collaborate with data engineering squads to define data requirements and work with structured and unstructured datasets using Python, SQL, Spark, and Pandas. - DevOps & Cloud Integration: Partner with cloud engineers and DevOps teams to ensure secure, containerized, and scalable deployments across cloud environments (AWS, GCP, or Azure). - Research & Prototyping: Evaluate emerging AI tools, frameworks, and model compression techniques; run rapid feasibility prototypes to translate business needs into technical solutions. - Stakeholder Communication: Articulate complex technical concepts and AI capability demonstrations clearly to business partners and non-technical stakeholders. Qualifications - Python & ML Framework Proficiency: Advanced programming skills in Python, with hands-on mastery of data science and deep learning libraries including NumPy, Pandas, Scikit-learn, PyTorch, or TensorFlow. - Generative AI & NLP Depth: Proven experience developing, fine-tuning, or deploying Large Language Models (LLMs), Natural Language Processing (NLP), or deep learning architectures. - MLOps & Containerization: Strong, practical knowledge of MLOps utilities and container orchestrators such as MLflow, Kubeflow, Airflow, Docker, and Kubernetes. - Cloud Platform & Model Deployment: Direct experience deploying machine learning models to production on major cloud platforms (AWS, GCP, or Azure). - Software Engineering Fundamentals: Solid understanding of data structures, algorithms, object-oriented design, microservices, and software engineering best practices. - Soft Skills: Outstanding verbal and written communication skills; strong analytical and problem-solving mindset; self-motivated, adaptable self-starter with exceptional time management and documentation skills. Preferred Assets & Nice-to-Haves - Prior experience within a Tier-1 Bank, Financial Institution, or Fintech enterprise. - Direct experience working within an Agile / Scrum delivery environment.