Technergetics are hiring a AI/ML Engineer III

AI/ML Engineer III

Technergetics — Utica/Rome, NY area

Opportunity Overview

Technergetics is looking for an AI/ML Engineer III to design, develop, and deploy advanced AI capabilities alongside a high-performing team of full-stack developers. This role centers on building production systems around foundation models, including agentic workflows, retrieval-augmented generation, and multimodal machine learning, for demanding government and commercial customers.

Contingent Position: This position is contingent upon contract award and funding.

Position Details

Salary Range: $125,000–$175,000 annually. The final offer depends on how many position qualifications the candidate meets, as well as education and experience. This is a full-time, exempt position.

Location, Travel, and Remote Work

• Candidates who are located within, or relocate to, a commutable distance of the Utica/Rome area can expect to be onsite 20% of their workweek, for access to company and AFRL (Air Force Research Lab) facilities, secure data, and customers. A relocation signing bonus may be available.
• Remote candidates outside a commutable distance to Utica/Rome will still be considered but may need to travel to the Utica/Rome area quarterly or more often, depending on company and client needs.
• This position also involves approximately 5%–10% travel to customer and client sites outside the Utica-Rome, NY area.

Due to the security clearance required for this position, only U.S. citizens are eligible to apply, per Executive Order 12968 (Access to Classified Information).

Responsibilities and Duties

The successful candidate will work on one or more of our Machine Learning (ML) software products, with day-to-day activities that include:

• Leading the design, development, and deployment of multi-modal machine learning architectures, including models and algorithms, to solve complex mission and business problems
• Designing and building agentic systems on top of large language models for operational deployment
• Implementing retrieval-augmented generation pipelines that ground model outputs against authoritative data sources
• Defining and running evaluation for model and agent behavior in production
• Optimizing model inference for production and edge/DDIL (denied, degraded, intermittent, and limited bandwidth) deployment scenarios
• Applying AI assurance practices and documenting model limitations to support accreditation and customer review
• Integrating state-of-the-art machine learning libraries, foundation models, and agent frameworks into existing software applications
• Developing and maintaining data pipelines and supporting software for collecting, preprocessing, and transforming data for machine learning tasks
• Designing software solutions, algorithms, and cloud architectures needed to satisfy product features and functionality defined by the product owner and other stakeholders in a production environment
• Leading, coaching, and mentoring junior data scientists, engineers, and other staff
• Contributing to phases of the software development life cycle, including functional analysis, technical requirements, technical design, prototyping, coding, testing, deployment, data migration, and support
• Participating in daily scrums and working with the scrum master and scrum team to organize and prioritize workload through story-pointing, supporting delivery timelines and priorities
• Collaborating with cross-functional teams to understand business requirements and translate them into machine learning solutions
• Performing unit testing and debugging to identify and fix software defects, and contributing to code reviews with constructive feedback to peers
• Staying current on new AI/ML approaches, frameworks, and industry trends
• Serving as an AI subject matter expert for small teams of researchers and engineers on advanced R&D projects funded by government and/or commercial customers, and contributing to or leading proposal writing for new opportunities within your area of expertise

Education and Certifications

This position generally requires a Master’s degree from an accredited college or university in computer science, computer engineering, artificial intelligence, machine learning, or a closely related discipline. A Ph.D. in one of these fields is strongly preferred. A Bachelor’s degree in one of these fields, combined with seven or more years of directly relevant professional experience, will be considered in lieu of a Master’s degree.

Qualifications

Experience and Foundational Engineering

• At minimum, three years of professional experience in machine learning or AI systems engineering, including at least one year building with large language models or other foundation models in a production setting
• Strong proficiency in Python, including asynchronous programming and modern packaging and dependency management
• Working knowledge of server-side development (API definitions, REST services, streaming and asynchronous services, etc.)
• Fluency with containerization and deployment frameworks such as Kubernetes or Docker, including GPU scheduling and resource management for training and inference workloads
• Hands-on work with at least one major cloud platform (AWS, Azure, or Google Cloud)
• Comfort with Linux platforms and command-line environments
• Familiarity with Continuous Delivery/Continuous Integration (DevSecOps, GitLab Pipelines, etc.)
• Proficiency with automated testing in Python (pytest), including regression suites for non-deterministic model and agent behavior

Artificial Intelligence and Machine Learning

• Demonstrated ability to train and deploy machine learning models with PyTorch and the Hugging Face ecosystem (transformers, datasets, accelerate)
• A track record of building applications on top of large language models, including prompt engineering, structured output, and context management
• Fluency with agentic frameworks and patterns such as LangGraph, LangChain, CrewAI, AutoGen, Pydantic AI, or vendor agent SDKs, including multi-step tool use, planning, memory, and state management, and with integrating models, tools, and data sources through open standards such as Model Context Protocol (MCP)
• Practical command of retrieval-augmented generation, including chunking and embedding strategy, vector databases (pgvector, Milvus, Qdrant, Weaviate, FAISS), and hybrid or re-ranked retrieval
• Ability to design and run LLM evaluation, including task-specific benchmarks, golden datasets, LLM-as-judge methods, and tracing and observability tooling (LangSmith, Langfuse, Arize, Weights & Biases)
• Command of model adaptation techniques including fine-tuning, LoRA/PEFT, quantization, and distillation, and the judgment to know when adaptation is preferable to prompting or retrieval
• Proven ability to serve models in production with inference frameworks such as vLLM, TensorRT-LLM, Triton Inference Server, Ollama, or ONNX Runtime, including latency, throughput, and cost tradeoffs, as well as deployment to edge or resource-constrained environments, including on-device inference
• Grounding in AI safety and assurance practices, including guardrails, input and output filtering, prompt injection mitigation, and human-in-the-loop design
• Direct work with multimodal models and cross-modal embedding across text, imagery, video, audio, or geospatial data

Leadership and Collaboration

• Demonstrated leadership on technical tasks and/or technical teams, including Agile software development and leading one or more tasks to completion
• Excellent communication and teamwork skills, including the ability to explain technical tradeoffs to non-technical stakeholders and customers

Nice to Have

• Exposure to DoD cloud and software factory environments such as Platform One, BESPIN, AF Cloud One, DAF CLOUDworks, or AWS GovCloud, and with ATO and RMF processes at IL4/IL5
• Familiarity with DoD and federal AI policy and governance, including CDAO Responsible AI guidance
• Work with distributed training frameworks and schedulers (DeepSpeed, FSDP, Ray, Slurm)
• Knowledge of knowledge graphs, ontologies, or Resource Description Framework (RDF), particularly as applied to graph-based retrieval (GraphRAG) and grounding
• Background in developing modern full-stack web applications with frameworks such as Node, React, React Native, or Django
• Basic working knowledge of Go, Java, C++, or other compiled languages
• Contributions to open source AI/ML projects, or published applied AI research

Clearance

Selected applicants will undergo a security investigation and must meet and maintain eligibility for, at minimum, Top Secret access to classified information.

Benefits

Our benefits package includes health, life, disability, dental, and vision insurance, plus a 401(k) plan with a 3% company contribution and 3% company match.

Additional perks include:

• Generous Paid Time Off, including a PTO “gift day” for your birthday
• 11 federal holidays per year
• Three weeks of paid maternity/paternity leave
• Annual technology allowance
• Referral bonuses and professional recognition awards
• Healthcare stipends
• Tuition/education reimbursement (once eligibility requirements are met)
• Flexible daily start and stop times for most projects and positions

Company Description

Technergetics is a U.S.-based company headquartered in Utica, NY, with employees and clients located throughout the country. The Utica/Rome area is a hub of cutting-edge cyber technology research, bolstered by the Griffiss Business & Technology Park's tenants and facilities, including the Air Force Research Lab (AFRL). At Technergetics, we work with a wide variety of technologies, including mobile and web apps, quantum computing, machine learning and artificial intelligence, AI-enabled edge devices, and more.

Technergetics does not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.