GenAI Forward Deployment Engineer

Role Summary :

We are seeking a highly motivated GenAI Forward Deployment Engineer to bridge the gap between business problems, AI technology, and production deployment. This role combines software engineering, Agentic AI development, LLM engineering, model training, dataset management, and solution deployment expertise. The ideal candidate will work directly with business stakeholders to understand challenges, rapidly prototype AI solutions, build production-grade Agentic AI applications, and drive successful adoption of AI capabilities across enterprise workflows. This role requires a hands-on engineer capable of taking AI solutions from concept to production, including data preparation, model development, prompt optimization, agent orchestration, deployment, monitoring, and continuous improvement.

Experience :

- 7 to 12 years of software engineering experience.

- 3+ years of experience developing AI/ML or GenAI solutions.

Skills :

- Strong programming expertise in : Python, JavaScript / TypeScript, SQL.

- Experience building enterprise-grade applications and APIs.

- Strong understanding of cloud-native architectures and distributed systems.

Interview Process :

- Total 3 Technical rounds.

- 2 rounds evaluation with LV (we can plan to take this together based on panel availability) & 1 with client.

Key Responsibilities :

1. Forward Deployment & Business Engagement :

- Partner directly with business stakeholders, SMEs, and product teams to understand business processes and identify AI automation opportunities.

- Translate business requirements into scalable Agentic AI solutions.

- Lead workshops, solution discovery sessions, and technical demonstrations.

- Drive successful implementation, user adoption, and business value realization.

- Act as the primary technical owner during pilot, rollout, and production phases.

2. Agentic AI Solution Development :

- Design and develop autonomous AI agents capable of reasoning, planning, tool usage, retrieval, and multi-step task execution.

- Build multi-agent workflows that automate complex business processes.

- Implement memory management, orchestration logic, guardrails, and human-in-the-loop workflows.

- Develop agent evaluation and monitoring frameworks.

- Optimize agent performance, reliability, and scalability.

3. LLM Engineering & Prompt Development :

- Design, test, and optimize prompts for enterprise use cases.

- Build Retrieval-Augmented Generation (RAG) architectures.

- Develop prompt libraries, agent instructions, and reusable AI workflows.

- Evaluate model outputs and continuously improve response quality.

- Implement AI safety, governance, and confidence scoring mechanisms.

- Fine-tune user interactions and agent behaviors to maximize business outcomes.

4. Model Training & Fine-Tuning :

- Prepare training datasets for AI and machine learning models.

- Support model fine-tuning, evaluation, validation, and optimization activities.

- Develop model performance benchmarks and evaluation metrics.

- Monitor model drift and retraining requirements.

- Collaborate with data scientists and AI engineers on model improvement initiatives.

- Manage model lifecycle activities from experimentation through production deployment.

5. Dataset Management & Knowledge Engineering :

- Build and manage enterprise datasets for AI solutions.

- Develop data ingestion and data quality frameworks.

- Create pipelines for collecting, cleansing, labeling, enriching, and maintaining structured and unstructured datasets.

- Build enterprise knowledge bases and semantic data repositories.

- Develop metadata, taxonomy, and knowledge management frameworks.

- Ensure data governance, security, compliance, and traceability standards are met.

6. Information Extraction & RAG Development :

- Build AI-powered extraction solutions for : PDF Documents, Word Documents, Excel Files, HTML/Web Content, Emails, Images and Scanned Documents, Enterprise Applications, APIs and Databases.

- Create document intelligence pipelines.

- Develop chunking, embedding, indexing, and retrieval frameworks.

- Build vector-based search and knowledge retrieval systems.

- Improve retrieval accuracy and relevance through continuous optimization.

7. Full-Stack & Integration Development :

- Develop AI-enabled applications and user interfaces.

- Build backend services, APIs, and microservices supporting AI workflows.

- Integrate AI solutions with enterprise systems and business applications.

- Develop reusable frameworks, SDKs, and platform services.

- Implement secure authentication, authorization, and governance controls.

8. Deployment, Monitoring & Operations :

- Deploy AI solutions into enterprise environments.

- Build CI/CD pipelines and automated deployment frameworks.

- Monitor model performance, system health, agent behavior, and application usage.

- Perform root-cause analysis and production support activities.

- Leverage Agentic AI tools for automated troubleshooting, testing, and maintenance.

- Drive continuous improvement of deployed AI solutions.

Preferred Qualifications :

1. Agentic AI & LLMs :

- Hands-on experience building Agentic AI applications and autonomous agents.

- Experience with : Large Language Models (LLMs), RAG Architectures, Function Calling / Tool Use, Multi-Agent Systems, Prompt Engineering, AI Evaluation Frameworks, Knowledge Graphs.

2. Model Development :

- Model training and fine-tuning experience.

- Dataset curation and data annotation expertise.

- Model evaluation and performance optimization.

- Familiarity with MLOps and LLMOps practices.

3. Data & Knowledge Platforms :

- Vector Databases, Graph Databases, Enterprise Search Platforms, Knowledge Repositories, Semantic Search Solutions.

4. Cloud & DevOps :

- AWS / Azure / GCP, Docker, Kubernetes, CI/CD Pipelines, Monitoring and Observability Platforms.

Technical Skills :

- Agentic AI & LLM Engineering : Agentic AI, Multi-Agent Architectures, LLM Integration, Prompt Engineering, RAG, Tool Calling, Memory Management, AI Evaluation.

- Data & Model Engineering : Dataset Management, Data Labeling, Feature Engineering, Model Training, Fine-Tuning, Model Evaluation, Data Quality Management.

- Knowledge Engineering : Vector Databases, Embeddings, Semantic Search, Knowledge Bases, Document Intelligence, Information Retrieval.

- Software Engineering : Python, JavaScript, TypeScript, REST APIs, Microservices, Distributed Systems.

- Cloud & DevOps : Docker, Kubernetes, CI/CD, MLOps, LLMOps, Monitoring & Observability.

Key Skills & Requirements :

- Large Language Models (LLMs), RAG Architectures, Function Calling / Tool Use, Multi-Agent Systems, Prompt Engineering, AI Evaluation Frameworks, Knowledge Graphs.

Joining :

- Immediate / 15 Days.

Success Profile :

- Rapidly convert business problems into deployable Agentic AI solutions.

- Build and operationalize enterprise-scale AI agents and knowledge systems.

- Create and manage high-quality datasets that improve AI performance.

- Deliver production-ready AI applications with measurable business outcomes.

- Drive adoption of AI capabilities through strong stakeholder engagement and solution ownership.

- Serve as the critical bridge between business users, AI technology, and enterprise deployment.

(ref:hirist.tech)