AI Engineer

CyOpsPath Introduction

CyOpsPath is a high-priority & time sensitive job discovery and career intelligence platform designed to connect top-tier talent with immediate, mission-critical opportunities across the United States and India.

At CyOpsPath, we actively track high-priority roles to drastically improve response rates for candidates. When you consistently apply to 7 tailored job roles daily for 30 days through our platform, our track record shows you will receive direct candidate updates and hear back from client hiring teams within 45 days.

AI Engineer

Key Responsibilities

1. Model Development: Design, train, fine-tune, and evaluate machine learning, deep learning, and generative AI models (including LLMs) to address complex business problems.
2. System Integration: Integrate AI/ML models into scalable microservices and user-facing applications via RESTful APIs and modern data pipelines.
3. Data Engineering & Pipeline Management: Clean, structure, and preprocess large datasets; build robust data pipelines for model ingestion, training, and real-time inference.
4. Optimization & Performance: Monitor, evaluate, and optimize existing models for inference latency, accuracy, cost-efficiency, and system reliability.
5. Collaboration: Partner with software engineers, data scientists, product managers, and cross-functional teams to translate business requirements into technical AI solutions.
6. Best Practices & Governance: Implement evaluation benchmarks, prompt engineering strategies, guardrails, and compliance frameworks to ensure responsible, ethical, and secure AI deployment.

Qualifications & Requirements

1. Education & Experience: Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related technical field (or equivalent practical experience), along with 3+ years of professional software or AI development experience.
2. Programming Skills: Proficiency in Python, along with familiarity with languages such as C++, Java, or Go.
3. AI/ML Frameworks: Hands-on experience with frameworks like PyTorch, TensorFlow, or Scikit-Learn, as well as generative AI tools (e.g., LangChain, LlamaIndex, Transformers, vector databases like Pinecone or Weaviate).
4. Software Engineering: Strong knowledge of object-oriented design, REST APIs, Git, CI/CD pipelines, and microservices architecture.
5. Cloud & DevOps: Familiarity with cloud environments (AWS, GCP, or Azure) and containerization tools like Docker and Kubernetes.
6. Problem-Solving: Exceptional analytical, debugging, and critical-thinking skills.