BP OPTIMA Forward Deployed Engineer Trainee Intern ₹22,000/month

BP OPTIMA PTE. LTD. is hiring for the Forward Deployed Engineer Trainee (SLMs) internship. This is a 3-month work-from-home internship focused on AI engineering, Small Language Models (SLMs), LLMOps, and production AI pipelines.

The internship offers a stipend of ₹12,000 – ₹22,000/month, with a potential full-time offer up to ₹8 LPA after successful completion.

This is an excellent opportunity for candidates interested in:

• Machine Learning
• Generative AI
• LLMOps
• RAG Systems
• AI Deployment
• Production ML Pipelines

If you want hands-on experience working on real enterprise AI systems, this internship stands out.

About the Company

BP OPTIMA builds decision infrastructure for regulated enterprises.

The company helps businesses convert messy real-world data such as:

• Documents
• Images
• Logs
• Operational records

into automated, auditable decisions using AI and deterministic business rules.

Their AI stack combines:

• Language models
• Computer vision
• Policy engines
• Decision routing systems

This makes them highly relevant in the growing enterprise AI automation space.

Internship Overview

What is a Forward Deployed Engineer?

A Forward Deployed Engineer (FDE) is a technical engineer who works close to customers and product teams to build tailored solutions.

Unlike traditional ML roles, FDEs handle:

• Customer-specific deployment
• Model customization
• AI integration
• Production debugging
• Business logic alignment

This role combines:

• ML Engineer
• Backend Engineer
• AI Product Engineer

into one.

What are SLMs?

SLM stands for Small Language Model.

Unlike massive LLMs like OpenAI models or Anthropic models, SLMs are smaller and optimized for:

• Lower cost
• Faster inference
• Domain-specific tasks
• Edge deployment
• Enterprise use cases

SLMs are becoming popular because enterprises often prefer:

• Lower latency
• Better control
• Lower infrastructure cost

Key Responsibilities

Build AI Decision Systems

You will work with the founding team on live AI systems for enterprise clients.

These systems automate decisions using AI-powered reasoning.

Examples:

• Document verification
• Risk analysis
• Policy compliance
• Workflow automation

Train & Fine-Tune Models

You will help train and fine-tune domain-specific models for:

• Text understanding
• Image analysis
• Log analysis

Fine-tuning improves model performance for specialized tasks.

Important concepts include:

• Transfer learning
• LoRA
• PEFT
• Instruction tuning

Build End-to-End AI Pipelines

You will help create pipelines from:

Raw Data → Processing → Model Inference → Decision Output

Pipeline stages may include:

• Data ingestion
• Preprocessing
• Embeddings
• Retrieval
• Inference
• Guardrails
• Response generation

Production AI engineering heavily relies on pipeline design.

Work on RAG Systems

A major responsibility involves Retrieval-Augmented Generation (RAG).

RAG allows AI systems to retrieve external knowledge before generating answers.

Typical RAG pipeline:

1. User query
2. Convert query into embeddings
3. Retrieve relevant documents
4. Feed context to model
5. Generate response

This improves factual accuracy.

Experiment with Embeddings

Embeddings convert text into vectors for semantic understanding.

Applications:

• Search
• Recommendations
• Similarity detection
• Knowledge retrieval

Common embedding providers include:

• OpenAI
• Google
• Open-source embedding models

Model Evaluation & Failure Analysis

A critical AI skill is understanding why models fail.

You will analyze:

• Hallucinations
• Misclassification
• Retrieval failure
• Context loss
• Bias issues

Improving model accuracy depends on systematic evaluation.

Production Deployment

You will help move models from:

Notebook experiments → Production systems

This includes:

• API deployment
• Monitoring
• Scaling
• Cost optimization

Many ML engineers struggle here, so this experience is highly valuable.

Performance Optimization

Production AI systems must balance:

• Accuracy
• Speed
• Cost

Optimization techniques include:

• Quantization
• Model pruning
• Efficient inference
• GPU optimization

These reduce operational cost.

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Required Skills

Python

Strong knowledge of:

Python

is mandatory.

Python is the backbone of modern ML systems.

Important libraries:

• NumPy
• Pandas
• PyTorch
• Transformers

PyTorch

Experience with:

PyTorch

is highly preferred.

You should understand:

• Tensors
• Training loops
• Backpropagation
• Model evaluation

Machine Learning Fundamentals

You should understand:

• Neural networks
• Loss functions
• Optimization
• Training vs inference
• Overfitting

Strong fundamentals matter more than memorizing tools.

NLP Knowledge

Important NLP topics:

• Tokenization
• Embeddings
• Transformers
• Attention mechanism

This role heavily involves NLP workflows.

LLMOps

LLMOps is like DevOps for language models.

It includes:

• Prompt management
• Model monitoring
• Evaluation pipelines
• Deployment automation

This is one of the fastest-growing AI career paths.

Git & Version Control

You should know Git for:

• Collaboration
• Code review
• Branch management

Rust (Bonus)

Exposure to Rust is a plus.

Rust is increasingly used in:

• High-performance inference
• AI infrastructure
• Systems engineering

• Emerson Hiring Intern-Technology

Company Rating & Reviews

Overall Rating: ⭐⭐⭐⭐☆ (4.4/5)

What Candidates May Like

• Exposure to modern AI stack
• Work directly with founding team
• Real production AI systems
• Strong learning in LLM engineering
• WFH flexibility

Things to Consider

• Fast-paced startup environment
• High ownership expected
• Research + engineering workload can be demanding

Best for: Candidates serious about careers in AI Engineering, ML Systems, or LLM Infrastructure.

Stipend / Salary

Internship Stipend

Full-Time Salary (PPO)

Salary depends on:

• ML knowledge
• Coding ability
• AI project experience
• Production engineering skills

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Who Can Apply?

This internship is ideal for candidates who have:

✅ Strong Python skills
✅ ML project experience
✅ AI research curiosity
✅ Interest in GenAI/LLMs
✅ Problem-solving mindset

Preferred backgrounds:

• Computer Science
• AI/ML
• Data Science
• Software Engineering

• Wipro Hiring Trainee BE/B.Tech/MCA

How to Apply

Before applying:

• Update resume with AI projects
• Add GitHub portfolio
• Highlight ML internships/hackathons
• Mention fine-tuning or RAG work
• Prepare for ML technical interview

Candidates with strong hands-on AI projects usually stand out.

Disclaimer:
This information is collected from official/public sources for informational purposes only. Salary estimates are based on market research and may vary. We do not charge any fee for job updates and do not guarantee selection or recruitment. Candidates should verify details from the official source before applying.