- 経験
- 3年以上
- 給料
- —
- 求人情報
- 1
- 投稿済み
- 7日前
- 作業モード
- 在任中
- 教育
- B.Tech/B.E.
- 資格
- B.Tech / B.E. in any specialization; suitable for candidates with the relevant AI/ML engineering background and at least 3 years of experience.
- 再開する
- 応募必須
勤務地
仕事内容
About the Company
Amerisource Solutions is an IT consulting firm that combines skilled talent with seasoned leadership to deliver effective solutions quickly. The organization focuses on helping businesses succeed in the digital era and emphasizes a strong history of delivering on that promise.
Role Overview
This role is for an LLM Engineer in Hyderabad, India, with a focus on building, refining, and operationalizing open-source large and small language models for enterprise environments.
Responsibilities
- Adapt and improve open-source LLMs and SLMs so they fit enterprise requirements.
- Plan and run model validation, benchmarking, and performance testing exercises.
- Deploy models and support them in cloud, private, and on-premise setups.
- Design inference workflows and tune models for speed, scale, and efficiency.
- Work closely with AI engineering and data teams to embed models into production systems.
- Maintain model safety, monitoring, and dependable day-to-day operation.
Candidate Profile
The ideal candidate should have at least 3 years of experience in AI/ML engineering or a closely related software engineering role. Strong practical exposure to training and fine-tuning open-source LLMs or SLMs is important, along with experience in evaluation, optimization, and deployment. The role also calls for solid Python skills, familiarity with current AI/ML frameworks, and a good understanding of serving, inference, and production deployment practices.
Technical Environment
Experience with GPU-based systems and scalable AI infrastructure is preferred. Candidates should have worked with open-source models such as Llama, Mistral, Qwen, Gemma, or comparable alternatives. Practical exposure to Hugging Face, vLLM, Ollama, or similar open-source AI tools is valuable. Knowledge of quantization, optimization, or inference acceleration will be an added advantage.
Enterprise Requirements
An understanding of secure enterprise AI deployment and responsible AI practices is expected, especially for environments that require privacy, control, and operational reliability.