Machine Learning Threat Intelligence Engineer
Explicitly requires vibe coding skills and use of AI code generation tools for rapid prototyping and PoCs.
About the Role
Design and deliver AI and Generative AI solutions as a Technical AI Solution Engineer/ML Threat Intelligence Engineer, building PoCs/MVPs and leading solutions from ideation to production. Partner with customers and product teams to translate business problems into scalable AI architectures and integrations.
Job Description
Role
Seeking a Technical AI Solution Engineer / Machine Learning Threat Intelligence Engineer to design, architect, develop, and deliver AI and Generative AI solutions. The role focuses on rapidly building PoCs/MVPs, leading full solution lifecycles, and integrating LLM-based systems with enterprise environments.
Location & Contract
- Hybrid: Dallas, TX or Atlanta, GA
- Contract: 3–6 months
- Expected time commitment: ~3 to 6 hours per day
Key Responsibilities
- Lead end-to-end design of AI and GenAI solutions, including system architecture, data flows, model selection, and integration patterns.
- Partner with customers, product managers, and business teams to identify high-impact AI use cases.
- Evaluate AI technologies, frameworks, and vendors and recommend solution approaches.
- Build PoCs, MVPs, and production-grade features using vibe coding, LLMs, embeddings, retrieval, agents, and AI pipelines.
- Implement AI workflows such as MCP, RAG, fine-tuning, prompt engineering, agentic orchestration, and model optimization.
- Develop integrations with enterprise systems, APIs, data platforms, and cloud environments (AWS, Azure, Google Cloud Platform).
- Act as a subject-matter expert and advisor on modern AI/LLM architectures and infrastructure.
- Optionally conduct knowledge-sharing sessions, demos, and workshops with clients and internal teams.
Required Skills & Experience
- Strong hands-on experience with LLMs and GenAI frameworks (e.g., OpenAI, Azure OpenAI, Anthropic, Meta Llama).
- Experience building AI pipelines using MCP/RAG architectures, agents/multi-agent orchestration, prompt engineering, and fine-tuning or parameter-efficient training (LoRA, adapters).
- Proficiency in vibe coding and AI code generation tools.
- Proficiency in Python and/or Node.js, and experience with LangChain, Semantic Kernel, or LlamaIndex.
- Familiarity with ML frameworks such as PyTorch (TensorFlow optional).
- Experience with vector databases (Pinecone, Weaviate, Chroma, Redis Vector).
- Knowledge of cloud AI services: Azure, AWS, Google Cloud Platform.
- Strong understanding of API architectures, microservices, DevOps, CI/CD, and containerization.
- Experience translating business requirements into scalable AI solutions and interacting with customers.
- 5–10+ years in software or solution engineering; 2–3+ years in AI/ML or GenAI implementation.
- Bachelor’s or Master’s in Computer Science, Engineering, AI/ML, or a related field.
Preferred Qualifications
- Experience in telco, media, or large enterprise environments.
- Knowledge of data engineering, ETL pipelines, and feature stores.
- Experience with MLOps/LLMOps methodologies and tools.
- Exposure to vector search optimization, knowledge graphs, or reinforcement learning.
- Familiarity with responsible AI, security, governance, and compliance frameworks.
What We Offer
- Opportunity to build impactful AI solutions at scale in a collaborative environment.
Contact
For applications and inquiries: hirings@openkyber.com