LLM/ML Specialist
hace 1 semana
We're looking for a
hands-on LLM/ML Specialist
to design, build, and optimize
production-ready AI solutions
based on open-source large language models (such as
LLaMA
,
Mistral
, or
Gemma
) and
Retrieval-Augmented Generation (RAG)
systems.
This is an applied role
not academic
focused on
deploying, integrating, and maintaining
scalable AI solutions in real business environments.
You'll work closely with engineering teams to fine-tune models, optimize inference performance, integrate vector databases, and deploy models using
Google Cloud Platform (GCP)
or similar infrastructures.
Key Responsibilities
- Design and implement
LLM-based solutions
for production use cases (chatbots, assistants, document understanding, etc.). - Develop and optimize
RAG pipelines
using vector databases (e.g., FAISS, Pinecone, Chroma). - Fine-tune open-source models (e.g., LLaMA, Mistral) using
PEFT methods
like LoRA or QLoRA. - Implement
prompt engineering
strategies to improve accuracy and reliability. - Deploy models and pipelines on
GCP, AWS, or Azure
, ensuring efficiency and scalability. - Collaborate with backend and frontend teams to integrate LLM APIs into applications.
- Monitor and evaluate system performance using relevant metrics (latency, retrieval precision, hallucination rate, etc.).
- Stay updated on the latest advancements in
LLM inference
,
quantization
, and
retrieval optimization
.
Required Skills & Qualifications
- 2+ years
of hands-on experience working with
LLMs
(fine-tuning, API integration, or deployment). - Solid programming skills in
Python
, including experience with
PyTorch
and
Hugging Face Transformers
. - Experience with
LangChain
,
LLM APIs
(OpenAI, Anthropic, Gemini, etc.), and
vector databases
. - Understanding of
RAG architectures
,
embedding generation
, and
similarity search
. - Knowledge of
model optimization
,
quantization
, and
context window management
. - Experience deploying AI systems on
Google Cloud Platform (GCP)
or similar environments. - Strong communication and collaboration skills — ability to work in remote, distributed teams
Preferred Qualifications
- Experience with
PEFT methods
(LoRA, QLoRA, adapters). - Familiarity with
hybrid retrieval
(dense + sparse search). - Exposure to
Docker
,
Kubernetes
, or
CI/CD
for AI pipelines. - Contributions to
open-source AI
projects or frameworks. - Understanding of
AI ethics
and model limitations.
What We Offer
- Opportunity to work on
cutting-edge LLM and RAG systems
with real-world impact. - Access to
high-performance computing
and collaboration with senior AI engineers. - Fully
remote
role with flexible working hours. - Competitive compensation aligned with skill and experience.
- Career growth
in the applied AI space with clear progression opportunities. - Support for continuous learning through courses and conferences.
- Inclusive, global team culture focused on innovation and collaboration.
Salary: $1200USD
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