Senior React & Full-Stack Engineer

TechDigital Group; Richmond (VA); Presencial; USD 70.000 - 110.000; Jornada completa

Descripción de la vacante

An established industry player is seeking a skilled Full Stack Developer with strong expertise in React web development and a solid background in mainframe tools. This role involves designing user interactions, developing servers, and ensuring seamless database functionality. With a focus on problem-solving and independent work, you'll tackle complex technical challenges while enhancing application performance. Join a dynamic team where your contributions will directly impact the success of innovative projects and drive the future of technology solutions.

Formación

• 5+ years of hands-on experience in React web development.
• Strong knowledge of COBOL, MVS, CICS, and JCL.

Responsabilidades

• Design, code, test, and maintain application programs and interfaces.
• Monitor systems performance and execute disaster recovery procedures.

Conocimientos

Verbal and written communication skills

Problem solving skills

Customer service skills

Interpersonal skills

Ability to work independently

Troubleshooting issues

Knowledge of computer hardware and software

End-to-end systems development life cycles

SQL

Visual Basic

COBOL

MVS

CICS

JCL

React web

Micro Front end architecture

Educación

Bachelor's degree in computer science

Herramientas

Mongo DB

Google Cloud

Descripción del empleo

Experience Required
10+ years of hands on experience in AI/ML engineering, with strong depth in knowledge graphs, unstructured data processing, and generative AI systems.
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Role Summary
We are seeking a highly experienced AI/ML Engineer with a strong foundation in knowledge graph engineering and generative AI to design, build, and scale intelligent data pipelines that transform large scale unstructured data into enterprise grade Knowledge Graphs.
The ideal candidate will have deep experience in ontology modeling, entity resolution, probabilistic pattern matching, and agentic knowledge base enrichment, combined with strong expertise in LLMs/SMLs, fine tuning pipelines, and graph based reasoning systems.
This role involves architecting and delivering production grade AI systems that integrate LLMs with knowledge graphs, enabling contextual reasoning, anomaly detection, and intelligent automation at scale.
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Key Responsibilities
Knowledge Graph & Ontology Engineering
• Design, build, and maintain enterprise scale Knowledge Graphs from large volumes of unstructured data (text, documents, logs, PDFs, web data).
• Create and evolve ontologies using RDF/OWL, including:
o Entity extraction and linking
o Entity resolution and disambiguation
o Probabilistic pattern matching
o Ontology alignment across heterogeneous data sources
• Implement semantic modeling for complex domains to support reasoning, discovery, and analytics.

Agentic Knowledge Base Enrichment
• Develop agentic AI systems for:
o Automated data gap identification
o Knowledge base enrichment and validation
o Continuous learning and self improving graph pipelines
• Build workflows that combine LLM reasoning with graph traversal and inference.

AI/ML & GenAI Systems
• Design and implement AI/ML pipelines integrating:
o Large Language Models (LLMs)
o Small Language Models (SMLs)
o Reasoning and task specific models
• Build fine tuning pipelines, including:
o Dataset generation and curation
o Training and fine tuning (SFT, PEFT, adapters)
o Evaluation, benchmarking, and deployment
• Apply prompt engineering, RAG, and hybrid LLM + Knowledge Graph (GraphRAG) techniques for contextual intelligence.

Anomaly Detection & Analytics
• Develop anomaly detection systems on top of knowledge graph data at scale.
• Apply graph analytics, embeddings, and ML techniques to detect:
o Semantic inconsistencies
o Behavioral anomalies
o Data quality and relationship drift

Data & ML Engineering
• Build robust data pipelines that ingest, process, enrich, and publish knowledge graph data.
• Implement scalable ML systems using Python for:
o Model development
o Training and tuning
o Inference and deployment
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Technical Skills & Expertise
Core AI/ML
• Strong AI/ML engineering background with deep expertise in:
o Python
o Model development, training, tuning, and deployment
• Extensive hands on experience with:
o Large Language Models (LLMs)
o Small Language Models (SMLs)
o Generative AI and reasoning models
o Text generation, summarization, and semantic search workflows
Knowledge Graph Technologies
• Strong experience with:
o Neo4j, GraphDB
o RDF, OWL
o Cypher, SPARQL
• Proven ability to implement:
o Entity linking and resolution
o Semantic search
o Relationship mapping and inference

GenAI Frameworks & Tooling
• Experience building GenAI systems using:
o LangChain, LangGraph
o LlamaIndex
o OpenAI / Azure OpenAI
o Vector databases such as Pinecone and FAISS

MLOps & LLMOps
• Strong experience in MLOps and LLMOps, including:
o MLflow, Azure ML, Datadog
o CI/CD automation for ML systems
o Observability, logging, and tracing
o Model performance monitoring and drift detection
• Experience deploying and operating AI systems in production environments.

Cloud & Scalability
• Experience building and optimizing AI/ML and graph pipelines either of any on:
o Azure
o AWS
o GCP
• Strong understanding of distributed systems, scalability, and performance optimization.
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Client is looking for candidates who have experience in building:
• Ontology from large scale data (requires experience in entity resolution, probabilistic pattern matching)
• Agentic knowledge-base enrichment (automated data gap identification, and data enrichment)
• Anomaly detection on top of knowledge graph data at scale
• Fine tuning pipeline (including dataset generation, tuning, evaluation, deployment) for small language models and reasoning models