
AI ML Full Time Interns
16 hours ago
Job Description
At SarvM AI, we're improving our award-winning Agentic AI solutions that act autonomously, reason deeply, and execute complex tasks with minimal supervision. We're looking for passionate minds to join our mission and shape the future of next-gen AI agents, LLMs, and prompt-driven intelligence.
The ideal candidate will be passionate about artificial intelligence and stay up to date with the latest developments in the field.
demo
Responsibilities
- Design, develop, and fine-tune
prompting strategies
for agent behavior, task orchestration, and complex multi-agent workflows. - Build LLM-centric pipelines that include
fine-tuning
,
RLHF
,
post-training optimization
, and
persona alignment
for domain-specific agents. - Collaborate with cross-functional teams to translate business objectives into intelligent agent workflows using data + context-driven prompting.
- Lead the full ML pipeline: from
data acquisition
,
cleaning
, and
preprocessing
to training, validation, and production deployment. - Prototype and deploy
autonomous AI agents
powered by internal knowledge bases, APIs, and third-party tool integration. - Develop evaluation benchmarks to assess performance, bias, and robustness of LLMs and agents in real-world conditions.
- Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
- Managing available resources such as hardware, data, and personnel so that deadlines are met
- Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
- Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
- Verifying data quality, and/or ensuring it via data cleaning
- Supervising the data acquisition process if more data is needed
- Finding available datasets online that could be used for training
- Defining validation strategies
- Defining the preprocessing or feature engineering to be done on a given dataset
- Defining data augmentation pipelines
- Training models and tuning their hyper parameters
- Analyzing the errors of the model and designing strategies to overcome them
- Deploying models to production
Skills We Value:
- Experience with
prompt engineering
,
LLMs, LangChain
, or similar agent frameworks. - Familiarity with prompting,
Chain-of-thought (CoT)
prompting,
RLHF
,
LoRA
,
fine-tuning
techniques, and knowledge distillation. - Python mastery, along with libraries like
scikit-learn
,
pandas
,
PyTorch
or
TensorFlow
. - Solid understanding of validation, data augmentation, hyperparameter tuning, and model performance metrics.
- Comfortable visualizing, manipulating, and debugging large-scale datasets.
- Hands-on experience in deploying ML models and agent-based apps to production environments.
- Some experience with a
deep learning framework such as PyTorch, TensorFlow. - Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
- Expertise in visualizing and manipulating big datasets
- Proficiency with OpenCV
- Familiarity with Linux
- Ability to select hardware to run an ML model with the required latency
Bonus:
- Understanding of
Toolformer/Function Calling
,
multi-agent coordination
, and
AI safety alignment - Exposure to
retrieval-augmented generation (RAG)
or
knowledge-grounded systems
As a standard process, selected candidates after all the rounds will be required to undergo an evaluation/ Training period (1-3 months) for confirmation of internship which is generally unpaid. However, If the performance is exceptional, the internship can become a paid one starting from the second month itself. Minimum internship duration is 6 months. Stipend Range between 10-20 K depending upon Performance
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