04 Aug
|
SysTechCorp
|
Hyderabad
04 Aug
SysTechCorp
Hyderabad
Company Description SysTechCorp Inc delivers leading-edge technology services that help clients achieve and surpass their strategic business goals. The company’s AI, ML, Mobile, Cloud, and ERP practices are built on years of hands-on experience and solid domain knowledge across multiple industry verticals. SysTechCorp focuses on solving complex business problems through scalable, secure, and innovative technology solutions.
Team members collaborate closely with global clients, gaining exposure to diverse projects and modern engineering practices. The organization values continuous learning, technical excellence, and practical impact. Job Summary
We are seeking an experienced AI/ML Engineer with strong expertise in Generative AI, semantic modeling, Knowledge Graphs, and agentic workflows. In this role, you will design, build, and deploy production-grade LLM applications, RAG pipelines, and multi-agent systems. You will turn structured, semi-structured, and unstructured data into semantic-aware, reliably grounded AI products.
The ideal candidate brings hands-on experience in ontology-based query expansion, Knowledge Graph construction (Neo4j), LLM grounding, stateful agent orchestration (LangGraph), and robust MLOps practices. Key Responsibilities
Generative AI & Agentic Systems: Design and deploy multi-agent workflows, stateful LangGraph orchestrations, tool-calling pipelines, and RAG architectures (including RAG-Fusion, hybrid search, and reciprocal rank fusion).
Semantic Modeling & Grounding: Build ontology-based query expansion, taxonomy-style controlled vocabularies, and semantic value resolution mechanisms to ensure high-accuracy LLM grounding and reasoning over heterogeneous data schemas.
Knowledge Graphs & Vector Databases: Construct, manage, and query Knowledge Graphs (Neo4j) alongside vector search engines (OpenSearch, Pinecone, Chroma) to support advanced retrieval and entity resolution.
Schema
Mapping & Text-to-SQL:
Implement AST parsing (e.g., SQLGlot), schema validation, and deterministic query/SQL generation with strict constraint rules (Pydantic/YAML).
Predictive ML & NLP: Develop predictive analytics, time-series anomaly detection, and classification models combining structured numerical features and unstructured text (e.g., XGBoost, TF-IDF, LSTM, CNNs).
LLM Evaluation & Reliability: Utilize structured evaluation frameworks (RAGAS, BLEU, ROUGE, BERTScore) to benchmark faithfulness, relevancy, and context recall, continuously optimizing system accuracy.
MLOps & Infrastructure: Build, containerize, and deploy automated CI/CD pipelines and model registries on cloud platforms (AWS / Azure) using Docker, GitHub Actions, MLflow, and DVC.
Required Qualifications & Skills
Experience: 3+ years of experience as an AI/ML Engineer or Data Scientist delivering production-ready Generative AI systems and ML pipelines.
Generative AI & Agents: Strong expertise in LangGraph, LangChain, Agentic AI architectures (ReAct, Supervisor models), Prompt Engineering, and Fine-Tuning (LoRA/QLoRA).
LLMs & Frameworks: Hands-on experience with foundational models (OpenAI GPT-4, Claude Sonnet/Opus), APIs, and evaluation tools (RAGAS).
Semantic Search & Graphs: Deep understanding of Neo4j, Knowledge Graph construction, Semantic Search, Domain/Schema Mapping, and Entity-Relationship Modeling.
Databases & Vector Stores: Proficiency in SQL (PostgreSQL, MySQL), DynamoDB, and vector databases (OpenSearch, Pinecone, Chroma).
Machine Learning & Deep Learning: Proficiency in Python and ML libraries (Scikit-learn, TensorFlow, Keras, XGBoost) covering regression, classification, clustering, time-series analysis, and NLP.
MLOps & Cloud: Experience with Docker, CI/CD, MLflow, DVC, AWS services (EC2, S3, Lambda, API Gateway, CloudWatch, EKS, RDS), or Azure AI/ML environments.
Education: Master’s or Bachelor’s degree in Computer Science, Data Science, Mathematics, or a related quantitative field.
📌 Senior Artificial Intelligence And Machine Learning Engineer (Hyderabad)
🏢 SysTechCorp
📍 Hyderabad