AI Engineer

Location -Bangalore

Work Mode -Fully in-office, 6 days a week

Experience -3+ years in AI engineering, with LLM-based systems shipped to production

About the Role

● The AI Engineer will own systems that read, search and reason over project and tender documents.

● Inputs include large tender packages, scanned PDFs, BOQs, tables, forms and engineering drawings such as P&IDs.; Outputs should include accurate answers with source references, structured data and workflows used by estimators and project teams in live bids, planning, procurement and execution.

● The role covers taking features from initial experiments to production and owning their accuracy, latency and cost after release.

Key Responsibilities

Agents and Retrieval

● Build AI agents that plan and execute multi-step tasks using document search, tools and APIs.

● Build RAG pipelines covering ingestion, retrieval, reranking and answers with source references.

● Use agent workflows within retrieval to refine searches, collect supporting evidence and verify outputs.

Document Intelligence

● Build extraction pipelines for PDFs, scanned documents, tables and forms.

● Convert unstructured tender content into structured data that the platform can use.

Vision and Image Understanding

● Build image-understanding workflows for P&IDs;, including symbols, equipment, labels and connections.

● Locate and remove selected elements from diagram images while preserving surrounding content.

Quality and Reliability

● Create evaluation datasets and automated checks to measure accuracy and catch regressions.

● Debug failures across retrieval, agent decisions, tool calls and model outputs.

Product and Production

● Build backend services and REST APIs. ● Integrate AI features into the Workwise web application.

● Take features from experiment to production while managing reliability, latency and cost

Required Qualifications

● 3+ years of hands-on experience building AI systems, including at least one LLM-based application running in production.

● Strong Python and software engineering fundamentals.

● Experience with backend services, REST APIs, databases, authentication and frontend integration.

● Experience building applications using LLM APIs.

● Working knowledge of RAG, embeddings, retrieval and vector databases.

● Experience with tool calling, structured outputs and multi-step agent workflows.

● Working understanding of multimodal models and image processing for visual analysis and editing.

● Systematic evaluation skills: measuring results and investigating system failures.

● Ownership of implementation, testing, deployment and ongoing improvement.

Good to Have

● OCR, document layout analysis or complex table extraction.

● Computer vision experience, including object detection, segmentation or image inpainting.

● Familiarity with construction, procurement or tender documents.

● Cloud infrastructure and production monitoring.

● Working knowledge of Go. 

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

AI Engineer

Experience: 2–4 Years

Employment Type: Full-Time

Work Mode: Coimbatore / Hybrid

About Techjays

Techjays is an AI Reimagination Company focused on building intelligent, scalable, and impactful software solutions. We help businesses solve complex challenges through AI, automation, and modern software engineering.

We are looking for an AI Engineer who is passionate about building AI-powered applications and integrating modern AI technologies into real-world products.

Key Responsibilities

• Design, develop, and deploy AI-powered applications using Python and modern AI frameworks.
• Build and implement LLM-based applications, including Retrieval-Augmented Generation (RAG) systems.
• Work with LLM APIs such as OpenAI, Gemini, and Anthropic.
• Develop AI agents and workflows using frameworks such as LangChain and LangGraph.
• Integrate vector databases and implement efficient embedding and semantic search solutions.
• Develop scalable backend services and REST APIs.
• Work with databases, data processing pipelines, and cloud-based services.
• Collaborate with frontend and backend teams to integrate AI capabilities into applications.
• Evaluate AI model performance, improve response accuracy, and optimize application performance.
• Debug, test, and maintain production-grade AI applications.

Required Skills

• 2–4 years of experience in software development, AI engineering, or a related field.
• Strong programming knowledge in Python.
• Hands-on experience with LLMs and Generative AI applications.
• Understanding of RAG architecture, embeddings, and semantic search.
• Experience with frameworks such as LangChain, LangGraph, or LlamaIndex.
• Knowledge of vector databases such as Pinecone, FAISS, ChromaDB, or Qdrant.
• Experience in building REST APIs using FastAPI or Flask.
• Good understanding of SQL and NoSQL databases.
• Familiarity with Git, Docker, and basic cloud services.
• Strong problem-solving and analytical skills.

Good to Have

• Experience building AI agents and multi-agent systems.
• Knowledge of React or other frontend frameworks.
• Exposure to AWS, Azure, or GCP.
• Understanding of model evaluation, prompt engineering, and fine-tuning.
• Experience deploying AI applications in production environments.

What We’re Looking For

• Someone who enjoys solving real-world problems using AI.
• Ability to take ownership of tasks and deliver solutions independently.
• Strong learning mindset and willingness to explore emerging AI technologies.
• Good communication and collaboration skills.

Job Title: Senior AI Engineer

Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred

Employment Type: Full-time

Department: Artificial Intelligence / Engineering

Location: Bangalore

We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.

The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.

Key Responsibilities

1. AI Solution Design and Development

• Design, develop, and deploy end-to-end AI and machine learning solutions.
• Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
• Develop scalable AI architectures and reusable components for enterprise applications.
• Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.

2. Generative AI and LLM Engineering

• Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
• Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
• Build AI agents and multi-step workflows using appropriate orchestration frameworks.
• Implement prompt engineering, structured outputs, tool calling, and context management.
• Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.

3. Machine Learning and Data Engineering

• Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
• Work with structured, unstructured, and multimodal data.
• Build data preprocessing, feature engineering, model training, and validation pipelines.
• Apply appropriate model evaluation techniques and statistical methods.

4. Production Deployment and MLOps / LLMOps

• Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Build automated CI/CD pipelines for AI applications and model deployment.
• Implement model versioning, experiment tracking, monitoring, and performance evaluation.
• Optimize inference latency, throughput, infrastructure utilization, and operating costs.
• Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.

5. Software Engineering and System Architecture

• Develop production-quality applications and APIs using Python and modern backend frameworks.
• Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
• Integrate AI services with enterprise applications, databases, and third-party systems.
• Use relational databases, vector databases, caching, and distributed systems effectively.
• Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.

6. Security, Quality, and Responsible AI

• Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
• Apply appropriate privacy, security, and compliance controls to AI systems.
• Evaluate hallucinations, bias, reliability, and failure modes.
• Design human-in-the-loop workflows and fallback mechanisms for critical use cases.

7. Technical Leadership and Collaboration

• Lead technical design discussions and mentor junior AI engineers.
• Define engineering standards, coding practices, and AI development guidelines.
• Collaborate with cross-functional teams to deliver measurable business outcomes.
• Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
• Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.

Required Technical Skills

Programming and Backend Development

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or Django.
• Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
• Experience with Git, automated testing, and CI/CD.

AI and Machine Learning

• Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
• Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
• Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.

Generative AI and LLMs

• Hands-on experience building production applications using LLM APIs or open-source models.
• Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
• Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
• Experience with tool calling, structured generation, agent workflows, and LLM evaluation.

Cloud and Infrastructure

• Experience with AWS, Azure, or Google Cloud.
• Working knowledge of Docker and Kubernetes.
• Familiarity with model serving, scalable APIs, monitoring, and production deployments.
• Experience with MLOps or LLMOps practices.

Databases and Data Processing

• Strong SQL skills and experience with relational databases.
• Familiarity with NoSQL databases, data pipelines, and distributed processing.
• Experience working with unstructured data, document ingestion, and retrieval systems.

Preferred Qualifications

• Experience with fine-tuning, quantization, or serving open-source LLMs.
• Experience with multimodal AI, including text, image, audio, or video.
• Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
• Experience with AI security, guardrails, governance, and responsible AI.
• Experience optimizing AI workloads for latency, scalability, and cost.
• Contributions to open-source AI projects, research, or technical publications.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Equivalent practical experience and demonstrated engineering expertise may be considered.

Key Success Metrics

• Quality, accuracy, and reliability of deployed AI solutions.
• Production adoption and measurable business impact.
• Reduction in inference costs and response latency.
• RAG retrieval quality and reduction in unsupported or incorrect responses.
• System availability, scalability, and operational stability.
• Delivery timelines, code quality, and technical mentoring effectiveness.

Title: AI Engineer - Product & Adoption Capability

Experience: 4-7 years

Location: Hyderabad

Mode of work: Hybrid

Role Summary:

P99Soft is hiring for an applied AI engineering role that also requires GTM-type capability - the ability to think about how AI tools actually get adopted by end users, not a dedicated go-to-market or marketing title. The engineer is expected to understand user workflows, spot adoption barriers, and shape how tooling gets rolled out, while remaining hands-on with applied AI work day to day.

Key Responsibilities:

• Build and support the rollout of AI-powered tools, driving awareness, onboarding, and adoption among target end users.
• Understand and map user workflows to identify where and why adoption of new AI tooling breaks down, and design solutions to close those gaps.
• Partner closely with engineering and architecture stakeholders to translate what's technically possible into what users will actually adopt and use.
• Gather and synthesize user feedback to inform product decisions and prioritization for the AI tooling roadmap.
• Apply hands-on applied AI knowledge to evaluate tool capability and limitations firsthand, rather than relying solely on engineering input.
• Communicate technical AI concepts to non-technical stakeholders in a way that drives buy-in and correct usage.

Required Experience & Skills:

• Applied, hands-on AI experience is non-negotiable - genuine exposure to building or working closely with generative/agentic AI systems, not just using AI assistants as an end user.
• Working GTM-type capability: comfort defining requirements, prioritizing based on user needs, and driving adoption of a tool - this can come from prior product exposure rather than a formal GTM title.
• Strong user empathy and the ability to identify practical adoption barriers for enterprise tools, distinct from purely technical blockers.
• Comfort operating with generative/applied AI systems (LLMs, agentic tools) as distinct from traditional data science approaches (regression, classification).
• Background can come from startup, founder, or self-taught paths — a traditional data science or enterprise pedigree is not required, provided applied AI fundamentals are genuinely strong.

Nice to Have:

• Some depth in AI systems internals (e.g., exposure to debugging model-serving or library/version compatibility issues) is a plus, though this role does not require building custom training pipelines from scratch.
• Experience operating in a regulated, quality-driven, or highly technical enterprise environment.

Benefits

• Competitive salary and performance-based bonuses.
• Comprehensive health insurance.
• Paid time off and flexible working hours.
• Professional development opportunities and support for continuing education.
• Udemy Learning Licenses to enhance skills and stay up-to-date with the latest technology.

P99Soft is an Equal-Opportunity Employer

P99soft is an equal-opportunity employer. At P99soft, we are committed to providing equal employment opportunities regardless of job history, disability, gender identity, religion, race, color, caste, marital/parental status, veteran status, or any other special status. We stand against the discrimination of employees and individuals and are proud to be an equitable workplace that welcomes individuals from all walks of life if they fit the designated roles and responsibilities.

Life @ P99Soft

At P99soft, we believe in creating a work environment that balances professional excellence with personal well-being. Our vibrant office culture is built on mutual respect, inclusivity, and a shared passion for innovation. We host regular team-building activities, workshops, and social events to foster a sense of community and collaboration among our employees. Join us and be part of a team that values your contributions and supports your growth.

About Us

P99soft is a leading IT Services and IT Consulting company dedicated to making our customers' business operations seamless by providing them with digital services and applications using the latest technologies. We are committed to bringing excellence to all our creations. We need passion, empathy, and being customer-focused to achieve technical excellence. We believe in transparency through open communication and work with honesty and integrity while dealing with our customers and partners.

Our Vision: “To lead digital transformation for our customers, delighting them at every step, while empowering our employees and fostering success.”

Our Mission: “To attain our objectives within a framework of integrity, transparency, and respect for our clients, employees, partners, and the community, fostering mutual trust and sustainable growth.”

Journey towards digital transformations begins here. Our talented teams help you make informed decisions, enhance operation efficiencies, create state-of-the-art visual interfaces, and design dynamic workflows to bring a better customer experience. Our engineering teams take complete ownership of assigning the right talent and architecting solutions using the latest technologies.

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

2

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

3

Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

4

Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

5

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

6

Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

7

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

8

Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

9

Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

10

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

11

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

12

Mandatory (Company): B2B Tech/Consulting/B2B Software

Role Overview

We are looking for an experienced AI/ML Engineer with strong expertise in Python, Generative AI, LLMs, LangChain, and LangGraph. The candidate will be responsible for designing and developing AI-powered applications, intelligent agents, and scalable LLM-based solutions.

Key Responsibilities

• Design and develop AI/ML and Generative AI applications using Python and modern LLM technologies.
• Build LLM-based applications and AI agents using LangChain and LangGraph.
• Develop agentic workflows involving tool calling, memory, reasoning, and multi-step orchestration.
• Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or other foundation models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases.
• Work with embeddings, prompt engineering, semantic search, and document processing.
• Develop scalable APIs and backend services using Python, FastAPI, or Flask.
• Build and integrate AI solutions with existing applications and enterprise systems.
• Deploy and maintain AI/ML solutions on cloud platforms.
• Collaborate with data scientists, software engineers, and product teams to develop business-focused AI solutions.

Required Skills

• Strong hands-on experience in Python programming.
• Strong experience in AI/ML and Generative AI.
• Hands-on experience with LLMs and LLM-based application development.
• Strong experience with LangChain and/or LangGraph.
• Experience building AI Agents / Agentic AI workflows.
• Strong understanding of RAG, embeddings, vector databases, and prompt engineering.
• Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, or Azure AI Search.
• Experience developing REST APIs using FastAPI/Flask.
• Good understanding of Machine Learning, NLP, and deep learning concepts.
• Experience with Azure / AWS / GCP cloud platforms.

Good to Have

• Experience with multi-agent systems and agent orchestration.
• Knowledge of MLOps / LLMOps.
• Experience with Docker, Kubernetes, and CI/CD.
• Knowledge of LLM evaluation, monitoring, observability, and AI governance.
• Experience with Azure OpenAI, Azure AI Foundry, or AWS Bedrock.

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

2

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

3

Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

4

Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

5

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

6

Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

7

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

8

Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

9

Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

10

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

11

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

12

Mandatory (Company): B2B Tech/Consulting/B2B Software