Senior AI Developer

Senior AI Developer (Remote)

Job Summary:

We are looking for an experienced Senior AI Developer to lead the design, development, and deployment of our AI-driven solutions and services. This role involves establishing a robust in-house AI development pipeline and integrating cutting-edge generative AI capabilities into our products. The ideal candidate will have over five years of experience in the AI/ML domain across multiple facets, including natural language processing (NLP), data engineering, model deployment, and MLOps best practices.

As our Senior AI Developer, you will be responsible for building and scaling AI applications that leverage the latest in large language models and cloud technologies. You will work closely with our product, data engineering, and DevOps teams to ensure these AI solutions are robust, scalable, and seamlessly integrated into our platform, while upholding security and quality standards for our startup's rapidly evolving offerings.

Key Responsibilities:

• AI Solution Design & Integration: Lead the design, development, and integration of AI-driven solutions into our products, ensuring these systems are robust, scalable, and deliver meaningful value to users.

• Chatbot & Generative AI Applications: Build and integrate intelligent chatbot and generative AI applications using large language models (LLMs) to enhance user engagement and automate customer support tasks.

• Prompt Engineering & Workflow Orchestration: Design effective prompt engineering strategies and orchestrate multi-step AI agent workflows (using frameworks like LangChain or LangGraph) to enable LLMs to perform complex tasks and conversations.

• Data Pipelines & RAG Integration: Integrate vector databases and implement retrieval-augmented generation (RAG) techniques to provide LLMs with relevant contextual data, working with data engineers to incorporate enterprise data sources (e.g., Snowflake, Databricks) into AI pipelines.

• Model Deployment & MLOps: Deploy and manage machine learning models (including LLMs) in production on cloud infrastructure (Azure preferred or AWS), implementing MLOps best practices like CI/CD pipelines, containerization (Docker, Kubernetes), and automated testing to ensure reliable and scalable model deployments.

• Performance Monitoring & Optimization: Monitor the performance of AI models and services, recommend and implement optimizations (such as model fine-tuning or caching) to improve latency and accuracy, and troubleshoot any issues in the AI pipeline.

• Scalability & Cost Optimization: Design AI systems with scalability and efficiency in mind, and optimize resource utilization and cloud costs for both model training and inference workloads.

• Security & Compliance: Ensure AI solutions adhere to data privacy and security standards (e.g., GDPR) and implement appropriate access controls and governance to maintain compliance with organizational policies and ethical AI practices.

• Collaboration: Work closely with product management, data engineering, DevOps, and software development teams to ensure AI initiatives align with business goals and integrate seamlessly into existing workflows.

• Innovation & Best Practices: Stay up-to-date with the latest advancements in AI and machine learning (new models, frameworks, techniques) and industry best practices. Drive continuous innovation and improvements in our AI solutions and development processes.

Required Skills & Qualifications:

• Experience: Minimum of 5 years of experience in software development, including at least 3 years of hands-on work in AI/ML engineering and deploying models to production environments.

• LLM & NLP Expertise: Deep understanding of natural language processing and large language models, with hands-on experience building applications using OpenAI/Gemini APIs or similar LLM platforms.

• Vector Databases & RAG: Hands-on experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Azure AI Search) and implementing retrieval-augmented generation techniques to provide relevant context to AI models.

• Programming Skills:
  
  Strong expertise in .NET and python development, with hands-on experience using Microsoft Dev Box for cloud-based development environments and building modern web applications with Blazor Wasm. Skilled in integrating systems and services through Azure for streamlined deployment and management. Additionally, proficient in machine learning frameworks like TensorFlow or PyTorch, and familiar with AI tools such as Hugging Face Transformers and LangChain for developing LLM-driven applications.

• MLOps & DevOps: Experience with machine learning operations and DevOps practices for AI, including building CI/CD pipelines for model deployment, containerizing ML solutions with Docker/ Kubernetes, and automating model retraining and monitoring.

• Cloud AI Platforms: Proven experience deploying AI solutions on cloud platforms, preferably Microsoft Azure (Azure Machine Learning, Cognitive Services, etc.) or AWS, and leveraging cloud infrastructure for scalability and performance.

• Data Engineering: Familiarity with data pipeline and warehouse technologies (e.g., Snowflake, Databricks) and experience handling large datasets for model training and inference, working with data engineering teams to ensure data quality and availability.

• Problem Solving: Strong analytical and troubleshooting skills for complex AI systems, with a proactive approach to identifying issues, debugging model errors, and ensuring reliability of AI services.

• Collaboration & Communication: Excellent communication skills and ability to work effectively in cross-functional teams. Able to articulate complex AI concepts in simple terms to non-technical stakeholders and collaborate on innovative solutions.

Nice-to-Have Qualifications:

• Advanced Degree & Research: Master's or PhD in Artificial Intelligence, Computer Science, or a related field, with any research publications or open-source contributions in the AI/ML domain.

• Full-Stack Development: Familiarity with general software development (e.g., building web or mobile applications) and the ability to integrate AI components into front-end or back-end systems (experience with JavaScript/TypeScript frameworks (such as React or ) and backend technologies like , Go, or Rust is a plus.

• Big Data & Distributed Systems: Experience with big data processing and distributed computing frameworks (such as Apache Spark or Hadoop) for handling and processing large-scale datasets in AI projects.

• Monitoring & ML Observability: Familiarity with monitoring and logging tools (e.g., Azure Monitor, Prometheus, MLflow) for tracking model performance, data drift, and system health in production AI systems.