Tech Stack
AWSAzureCloudDockerDynamoDBFlaskGrafanaJavaScriptJenkinsKubernetesMongoDBMySQLNode.jsNoSQLPostgresPrometheusPythonPyTorchRayReactSQLTensorflowTypeScript
About the role
- Architect and implement agentic AI systems capable of autonomous decision-making, task planning, and execution in real-world applications
- Design and integrate multi-agent systems to solve complex problems through collaborative and competitive interactions
- Develop and fine-tune large language models (LLMs) and reinforcement learning (RL) models to power agentic behaviors
- Implement robust APIs and interfaces to integrate AI agents with external systems, databases, and tools
- Optimize AI models for performance, scalability, and low-latency inference in production environments
- Conduct rigorous testing, validation, and monitoring of AI agents to ensure reliability, safety, and alignment with ethical standards
- Collaborate with product managers, data scientists, and software engineers to define requirements and deliver end-to-end AI solutions
- Stay updated on latest advancements in Agentic AI, LLMs, and RL and incorporate cutting-edge techniques into development workflows
- Mentor junior developers and contribute to knowledge-sharing within the team
Requirements
- Clearance: Active TS/SCI within last 24 months
- Education: BA/BS in Computer Science or another related field
- Experience: BS + 10 Yrs or MS + 8 Yrs experience in computer science, AI, Machine Learning, or a related field
- 5+ years of experience in AI/ML development, with at least 2 years focused on Agentic AI or autonomous systems
- Proven track record of deploying production-grade AI systems, including framework experience such as AWS Bedrock
- Strong problem-solving skills and ability to work in a fast-paced, collaborative environment
- Preferred: PhD in computer science, AI, Machine Learning, or a related field
- Programming Languages: Python (primary), JavaScript/TypeScript (for API development), C++ (for performance-critical components)
- Frameworks and Libraries: PyTorch, TensorFlow, JAX
- Reinforcement Learning: Stable-Baselines3, Ray RLlib, OpenAI Gym, or Gymnasium
- Agentic AI Frameworks: LangChain, LlamaIndex, AutoGen, or CrewAI
- API Development: FastAPI, Flask, or Node.js
- Cloud Platforms: AWS (SageMaker, Lambda, Bedrock), Google Cloud AI, Azure AI
- Containerization: Docker, Kubernetes
- Version Control: Git, GitHub, or GitLab
- Databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, DynamoDB)
- DevOps Tools: CI/CD pipelines (Jenkins, GitHub Actions), monitoring tools (Prometheus, Grafana)
- AI Models and Techniques: Experience with LLaMA, GPT, BERT, Grok and leveraging AWS Bedrock
- Reinforcement Learning algorithms expertise (e.g., DQN, PPO, SAC) and multi-agent RL systems
- Knowledge of goal-driven agents, task decomposition, and autonomous planning (ReAct, Plan-and-Execute architectures)
- Prompt engineering experience and model fine-tuning techniques (LoRA, QLoRA, full fine-tuning)
- Familiarity with evaluation metrics such as BLEU, ROUGE, perplexity, and custom agent performance metrics
- Generous cost sharing for medical insurance for the employee and dependents
- 100% company paid dental insurance for employees and dependents
- 100% company paid long-term and short term disability insurance
- 100% company paid vision insurance for employees and dependents
- 401k plan with generous match and 100% immediate vesting
- Competitive Pay
- Generous paid leave and holiday package
- Tuition and training reimbursement
- Life and AD&D Insurance
ATS Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonJavaScriptTypeScriptC++PyTorchTensorFlowJAXStable-Baselines3Ray RLlibOpenAI Gym
Soft skills
problem-solvingcollaborationmentoring
Certifications
Active TS/SCI clearancePhD in computer scienceMS in computer science