Vanivation Corp.
Responsibilities AI Research & Development - Research, prototype, and evaluate state-of-the-art AI/ML techniques for applicability to internal and client-facing systems. - Design and develop custom machine learning models (supervised, unsupervised, reinforcement learning) tailored to business problems. - Build and maintain Retrieval-Augmented Generation (RAG) architectures for intelligent document search, knowledge management, and Q&A systems. - Develop predictive models for forecasting, anomaly detection, churn analysis, recommendation engines, and other applied ML use cases. AI Solutions Engineering - Architect and deploy AI-powered features and services that integrate seamlessly into existing product ecosystems. - Build multi-agent and agentic AI workflows - including computer use agents, browser automation agents, and tool-using agents via Model Context Protocol (MCP) - that go beyond simple call-and-response LLM integrations. - Implement and optimize LLM-powered pipelines using orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex). - Design vector database schemas, embedding pipelines, and semantic search systems. - Fine-tune open-source and proprietary foundation models (LLaMA 3, Mistral, Gemma 3, GPT-4o, Claude variants) for domain-specific tasks, including multimodal models capable of processing text, images, audio, and structured data. Production & MLOps - Deploy and monitor AI models in production environments using MLOps best practices (MLflow, DVC, or equivalent). - Build CI/CD pipelines for model deployment, retraining, and versioning. - Ensure model reliability, performance, fairness, and explainability across deployments. - Manage cloud-based AI infrastructure on AWS, GCP, or Azure including GPU/TPU workloads. Collaboration & Leadership - Translate complex business requirements into clear AI/ML technical specifications. - Directly manage a team of AI and software developers - overseeing hiring, onboarding, performance reviews, career development, and day-to-day workload prioritization for all direct reports. - Mentor junior and mid-level developers on AI best practices, tooling, and architecture, fostering a culture of continuous learning and technical excellence. - Partner with stakeholders to communicate model performance, trade-offs, and insights. - Stay current with AI research, industry trends, and emerging tools - systematically evaluating new foundation models, agentic frameworks, and AI infrastructure technologies to determine their suitability for integration into the company's AI model stack and product roadmap. - Lead structured technology evaluation processes for new AI models and platforms - defining evaluation criteria, running benchmarks, assessing cost/performance trade-offs, and making informed adoption recommendations to engineering and product leadership. - Resilient by nature and by practice-you bounce back fast, learn faster, and see challenges as invitations to experiment, iterate, and try again (and again) until it works. - Curiosity & Continuous Learning: Demonstrates a strong sense of curiosity by proactively asking questions, seeking to understand the "why" behind decisions, and exploring new ideas, tools, and approaches. Actively learns from feedback, experiments, and emerging best practices to continuously improve outcomes. Work Experience & Area Requirements - Based on current market standards, this role requires a minimum of 5 years of software development experience, with at least 3 years specifically focused on AI/ML engineering - including end-to-end model development and production deployment. - General Software Development: 5+ years (industry standard for Senior-level roles) - AI / ML Engineering (Production): 3+ years building and deploying ML models - LLM / Generative AI Development: 2+ years (RAG, agents, fine-tuning, orchestration) - Cloud Platform (AWS / GCP / Azure): 2+ years deploying AI/ML workloads at scale - MLOps & Model Lifecycle Management: 2+ years using tools like MLflow, DVC, or Kubeflow - People Management & Developer Leadership: 2+ years managing software or AI developers as direct reports (performance reviews, mentoring, hiring) Required Technical Skills - Strong knowledge of ML frameworks: TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers. - Hands-on experience with LLM orchestration: LangChain, LangGraph, LlamaIndex, OpenAI SDK, Anthropic SDK, and Model Context Protocol (MCP) for tool and agent integration. - Experience with vector databases: Pinecone, Weaviate, pgvector, ChromaDB, or Qdrant. - Solid understanding of RAG architecture, embedding models, chunking strategies, semantic retrieval, and advanced patterns such as hybrid search, re-ranking, and graph-augmented retrieval. - Experience with model fine-tuning techniques including LoRA, QLoRA, RLHF, and DPO (Direct Preference Optimization), as well as familiarity with reasoning model architectures (chain-of-thought, o1/o3-style models, and DeepSeek R1). - Proficiency in SQL/NoSQL databases and data preprocessing pipelines. - Working knowledge of Docker, Kubernetes, and containerized model serving. - Understanding of software engineering best practices: version control (Git), code review, testing, and CI/CD. Nice to Have - Experience with agentic frameworks including AutoGen, CrewAI, OpenAI Agents SDK, Anthropic's Claude Agent SDK, and Model Context Protocol (MCP) server development. - Background in NLP, computer vision, or multimodal AI applications. - Published research, open-source contributions, or portfolio of AI projects. - Familiarity with A/B testing, model monitoring, and data drift detection. - Knowledge of AI safety, responsible AI, model explainability (SHAP, LIME), and emerging AI governance frameworks including EU AI Act compliance considerations. Core Technology Stack - PyTorch / TensorFlow - LangChain / LangGraph / MCP - RAG Pipelines - Hugging Face - OpenAI / Anthropic API - Vector Databases - MLflow / DVC - Docker / Kubernetes - AWS / GCP / Azure - LLM Fine-Tuning & Reasoning Models - Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field. - Equivalent practical experience will be considered. Relevant certifications (AWS ML Specialty, Google Professional ML Engineer, DeepLearning.AI, etc.) are a plus. ABOUT VANIGENT - Vanigent is an independent contract sales organization delivering measurable, results-driven outcomes that prioritize the needs of our customers. We are an Atlanta, GA-based CSO, supported by a seasoned leadership team with deep expertise in commercial operations and sales execution. Our agile, customer-focused approach is grounded in our core values of C ustomer-centered solutions, A ccountability, R esults-oriented mindset and E thics, Excellence & Integrity (CARE). - Vanivation Corp. is the local independent entity in the Philippines of Vanigent. Focused on providing technology-driven solutions for pharma, diagnostics and medical devices industries. We deliver innovative solutions that optimize operations, ensure compliance and drive growth. - We are proud to be an affirmative action/equal opportunity employer, committed to diversity, equity, and inclusion. We do not discriminate based on race, color, religion, gender, gender identity, sexual orientation, national origin, protected veteran status, disability, or any other legally protected status.
Vanivation Corp.
