Technical Marketing Engineer ML Development and Solutions

Solidigms Technical Marketing Engineering (TME) team is expanding our AI enablement efforts to transform how we build manage and deliver technical content tools and workflows across the product lifecycle. We are seeking a hands-on ML Software Engineer with expertise in Large Language Models (LLMs) agent-based systems and custom model this role you will own the end-to-end full stack solutions to prototype implement and refine workflows that support TME deliverables internal tools and systems. This role offers ownership of high-impact ML initiatives shaping Solidigms AI transformation and strategy customer experience and internal engineering productivity while building deep expertise in next-generation datacenter storage and memory technologies and collaborating with world-class engineers and marketing leaders.

Key Responsibilities

• LLM Engineering & Model Development
  • Architect design and implement LLM-driven systems for domain-specific technical challenges
  • Develop and optimize prompt strategies for accuracy consistency and performance
  • Fine-tune benchmark and deploy proprietary and open-source LLMs for enterprise workflows
  • Design and build scalable secure and self-hosted AI solutions
• Agent & Tooling Development
  • Build AI-assisted tools that improve TME workflows including document generation content search summarization and knowledge access
  • Develop autonomous and semi-autonomous agents capable of multi-step reasoning
  • Design and deploy agents that plan tasks use tools reliably and adapt within defined constraints
  • Integrate external APIs internal systems and custom utilities into agent architectures
  • Develop dashboards workflow tools and automations using modern front-end frameworks
  • Implement feedback loops and usage analytics to guide iterative UX and model improvements
• Cross-Functional Collaboration
  • Partner with IT and enterprise data platform teams to ensure security governance and compliance
  • Collaborate across engineering marketing and operations to gather requirements and build scalable solutions
  • Document architectures design decisions and best practices for long-term knowledge sharing
  • Translate ambiguous problems into working proofs of concept and iterate with stakeholder feedback
  • Stay current with emerging AI techniques and mentor and influence the broader organization

Qualifications :

• Education
  • Masters degree in Computer Science Machine Learning Computer Engineering Data Science Software Engineering or related field (PhD preferred)
• Core LLM & Generative AI Expertise
  • Hands-on experience deploying and adapting open-source LLMs (e.g. Llama Qwen Mistral) for on-prem solutions as well as integrating API based cloud services. Demonstrated through projects coursework internships or open-source contributions.
  • Proven experience designing and deploying LLM-based applications
  • Strong command of advanced prompt engineering techniques (few-shot chain-of-thought system prompts)
  • Solid understanding of tokenization inference optimization and context window management
  •  Familiarity with RAG systems embeddings and vector database
• Agents Frameworks and ML Tooling
  • Experience building or fine-tuning agents using frameworks such as Dataiku TensorFlow or PyTorch
  • Familiarity with modern agent architectures (ReAct tool use memory systems long-horizon planning)
  • Experience with frameworks and libraries such as LangChain LlamaIndex or Hugging Face
  • Understanding of GPU utilization and optimization for ML workloads
• Software Engineering
  • Strong programming skills in Python (or similar) with production-quality coding practices
  • Understanding of core software development practices (Git testing code reviews documentation and CI pipelines)
  • Experience with Docker and cloud platforms (AWS Azure GCP)
  • Ability to build robust ML/AI pipelines data processing workflows or automation scripts
  • Ability to translate complex ML capabilities into polished intuitive user experiences
  • Interest in applied AI for engineering productivity and realworld impact rather than purely academic research
  • Exposure to enterprise or infrastructure technologies (e.g. storage networking systems) is a plus

• Collaboration:
  • Strong problem-solving skills and comfort working in ambiguous technical domains
  • Self-directed iterative and highly collaborative and growth mindset
  • Excellent communication and collaboration skills

Additional Information :

The compensation range for this role is $105440 - $164800. Actual compensation is influenced by a variety of factors including but not limited to skills experience qualifications and geographic location.

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Remote Work :

No

Employment Type :

Full-time