Inktel Contact Center Solutions

Job Summary
The AI Solutions Engineer at Nymbl delivers enterprise-grade AI solutions directly with clients. Acting as a forward-deployed engineer, this role blends full-stack development expertise, applied AI/ML engineering, and strong client-facing skills. AI Solutions Engineers implement Retrieval-Augmented Generation (RAG) systems, design and deploy LLM-powered applications, and integrate AI into enterprise workflows to create measurable client outcomes.

This role blends responsibilities from:

Forward-Deployed Engineer – client delivery, technical advisory, building in production environments.

Machine Learning Engineer – Fine tune, and deploy LLM and RAG systems with applied AI expertise.

Full-Stack Developer – enterprise-grade coding across front-end, back-end, and data layers.

Your Responsibilities

• Collaborate with clients to understand their business requirements and translate them into technical specifications.
• Design and implement scalable solutions using cloud technologies such as AWS.
• Develop machine learning models utilizing frameworks like TensorFlow, R, and Python for various applications including natural language processing and data mining.
• Perform ETL processes to integrate data from multiple sources into cohesive datasets for analysis.
• Conduct model training and deployment, ensuring optimal performance of machine learning applications.
• Utilize big data technologies such as Hadoop and Spark to process large datasets efficiently.
• Engage in database design and management, ensuring data integrity and accessibility using SQL and other database languages.
• Implement analytics solutions using tools like Looker for data visualization and reporting.
• Stay updated on industry trends, emerging technologies, and best practices in quantum engineering and AI.

Expectations

• Leadership: Take ownership of technical implementation, guiding both clients and internal teams toward scalable, production-ready AI solutions.
• Communication: Translate complex AI concepts into clear business and technical language for executives, stakeholders, and developers.
• Autonomy: Lead end-to-end delivery of AI features and integrations, managing coding, testing, deployment, and client handoff.
• Collaboration: Partner closely with Solution Architects, Client Partners, and Developers to ensure projects balance innovation, feasibility, and business value.
• Client Engagement: Act as a trusted technical advisor in workshops, demos, and delivery reviews, building confidence that Nymbl can execute reliably.

What You'll Do

• Design and implement RAG pipelines with LLMs and enterprise data sources.
• Build and deploy AI agents using frameworks such as LangChain, Semantic Kernel, or custom architectures.
• Develop full-stack AI-enabled applications (front-end, back-end, APIs, and data integrations).
• Optimize vector databases (e.g., Pinecone, FAISS, Milvus) for retrieval and semantic search.
• Fine-tune or adapt LLMs for industry- or client-specific needs.
• Deploy solutions with enterprise reliability standards (Docker, Kubernetes, CI/CD).
• Run client demos, technical workshops, and enablement sessions to accelerate adoption.
• Collaborate with internal teams on burn tracking, utilization, and project profitability.
• Document architectures, pipelines, and operational guidelines for client and internal use.

Common Challenges and Needed Skills

• Enterprise data complexity: problem-solving, unstructured + structured data pipelines.
• Rapidly evolving AI tools: continuous learning, adaptability, maturity assessment.
• Client skepticism about AI: clear communication, proof points, framing business value.
• Balancing innovation vs production-readiness: disciplined testing, pragmatic engineering mindset.
• Integration into legacy systems: creativity, patience, full-stack development skills.

Technical Skills

Prompt Engineering

● Crafting and iterating on prompts for LLMs to achieve consistent, accurate, and enterprise-ready outputs.

● Applying structured techniques to reduce variability and ensure responses align with client requirements.

Retrieval-Augmented Generation (RAG) Systems

● Designing pipelines that integrate vector databases, embeddings, and prompt templates.

● Connecting enterprise data sources into LLM-powered workflows for context-rich responses.

Model Fine-Tuning

● Applying supervised fine-tuning, reinforcement learning with human feedback (RLHF), or domain adaptation.

● Providing client-specific datasets to improve accuracy, compliance, and relevance.

_ AI Agents_

● Building autonomous agents that use reasoning + tools to act within client environments.

● Combining multiple LLM roles (e.g., planner, executor, validator) into reliable workflows.

_ LLM Deployment_

● Packaging and deploying LLM solutions into client production environments.

● Leveraging containerization, APIs, and deployment pipelines for scalability and security.

LLM Optimization

● Applying quantization, distillation, caching, and latency reduction techniques.

● Balancing model performance, cost efficiency, and client SLAs.

LLM Observability

● Implementing monitoring for model accuracy, bias, latency, and cost.

● Using tracing, dashboards, and evaluation frameworks to ensure reliability at scale.

Context Engineering

● Designing workflows that bring the right data (documents, memory, tools, databases) into prompts.

● Ensuring compliance, data governance, and high fidelity of client knowledge bases.

Job Type: Full-time

Benefits:

• 401(k)
• Dental insurance
• Health insurance
• Paid time off

Application Question(s):

• LinkedIn Link Required for Candidacy

Work Location: Remote